669results about "Microscopic object acquisition" patented technology

The invention discloses an aberration correction and image quality enhancement method for a laminated structure image, and the method comprises the steps: carrying out the deconvolution preprocessing of a to-be-detected marked image according to an aberration priori set, and obtaining an aberration-free image; meanwhile, combining label data to obtain a data set; feature extraction is carried out based on shallow convolution according to the data set, and global feature information is generated through activation function operation; enhancing the feature data by adopting a frequency domain feature and spatial domain feature fusion strategy; the enhanced feature map realizes initial aberration restoration through an aberration correction module; the corrected feature map is processed by a double-channel attention mechanism, and the global context modeling capability of the self-attention mechanism and the spatial perception characteristic of the position attention unit are fused in parallel; and the image resolution is improved through a super-resolution reconstruction module comprising a sub-pixel convolution layer. By adopting the technical scheme of the invention, the accuracy of overlay error detection is improved.

The invention provides a physical response data analysis method and system based on aircraft catalytic operation, and the method comprises the steps: obtaining three-dimensional cloud particle distribution data sets of a catalytic operation region before and after the catalytic operation through airborne cloud particle detection equipment, the three-dimensional cloud particle distribution data set comprises particle characteristic data before catalysis and particle characteristic data after catalysis; performing morphological feature extraction processing on the pre-catalysis particle feature data and the post-catalysis particle feature data by adopting a cloud particle AI image shape recognition algorithm to generate a pre-catalysis particle morphological difference feature set and a post-catalysis particle morphological difference feature set; constructing a dynamic physical response evaluation model, inputting the particle morphological difference feature set into the dynamic physical response evaluation model to execute multi-dimensional response correlation analysis, and generating a dynamic response feature set of the catalytic operation; and generating a catalytic strategy optimization instruction set according to the dynamic response feature set, and transmitting the catalytic strategy optimization instruction set to the airborne catalytic control system for catalytic parameter adjustment operation.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention belongs to the field of sealing element leakage image recognition, and particularly relates to a metal sealing element tiny leakage detection system and method based on infrared image recognition. A metal sealing element tiny leakage detection method based on infrared image recognition comprises the following steps that S10, the detection environment temperature is collected to serve as the background temperature, and the comparison temperature is determined according to the background temperature and the resolution ratio of an infrared camera; setting an air pressure threshold value, filling the closed space of the metal sealing element with steam of which the temperature is not lower than the contrast temperature, and monitoring the inflation air pressure of inflation to ensure that the inflation air pressure is not higher than the air pressure threshold value; and S20, acquiring image data from multiple angles by using infrared rays and ultrasonic waves, processing the acquired infrared image data, and identifying and marking a leakage range in the infrared image data. According to the scheme, the problem that the reconstruction precision and the calculation speed are unbalanced under the constraint of complex environmental noise and calculation resources in the prior art is solved, the leakage detection sensitivity and the positioning accuracy are improved, and the hardware dependence and the calculation amount are reduced.

The invention relates to the technical field of microscopic biological observation, in particular to a microscopic biological observation method based on a digital microscope. Firstly, original point cloud of a sample area on an objective table is obtained through structured light scanning, noise points are removed, point cloud density is optimized by adjusting scanning intensity for multiple times, high-precision and low-noise preprocessed point cloud is generated, then a reference plane is fitted in combination with the structured light point cloud, and obstacle and non-obstacle features are extracted. Detecting a second center coordinate of the bounding box by using a YOLO model, obtaining a third center coordinate through Hough circle detection, generating a standard coordinate by fusing multi-source coordinates, generating a global path based on a grid map and an RRT algorithm, predicting a track in real time, calculating a minimum Euclidean distance with an obstacle, dynamically inserting a B spline transit point to avoid a dangerous area, and ensuring a safe distance. Finally, collecting sample images in real time along the path and extracting microorganism characteristics; the technical problem that the efficiency is low when a traditional microscope is used for acquiring a microorganism image in a sample is solved.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

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.

Means for rigorous quantitative assessment of sperm motility are presented, based on a number of movement and morphology parameters measured using various image processing methods. The quantitative assessment thereby achieved is objective and multidimensional, allowing for detailed and repeatable assessment of samples. Deep learning methods are also disclosed for detection and classification of sperm cells (or other types of cells or particles) using neural networks, adapted to work both in good and in poor imaging condition, such as low magnification and resolution. Specific methods for both supervised and unsupervised deep learning approaches are delineated. In a non-limiting disclosure, the methods are particularly adapted to deal with cases of azoospermia, where there is a very low number of sperm cells (most of which do not swim) and a lot of debris in the imaged field of view.

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.

Described herein are systems, methods, and programming for analyzing and classifying digital pathology images agnostic to sample processing techniques used to prepare the digital pathology images. In some embodiments, image data including a first image set and a second image set may be obtained. The first and second image sets may be processed using a first and second slide preparation machine, respectively. A first augmented view set and a second augmented view set may be generated based on augmentations applied to the first and second image sets. For each image, a first vision transformer to may be trained to: generate a first representation of an augmented view of the first augmented view set, and enhance a similarity between the first representation and a second representation of an augmented view of the second augmented view set. The second representation may be generated via a second vision transformer.

The invention relates to the field of bioinformatics and computational biology, in particular to a method for realizing accurate alignment of biological slices by utilizing a spatial graph network and a cell communication network based on the biological slices of a graph attention network. The method mainly comprises the following steps: (1) acquiring a high-resolution biological slice image and spatial transcriptome data of a target tissue or brain region (such as DLPFC), and carrying out standardization processing on the high-resolution biological slice image and spatial transcriptome data; (2) constructing a space diagram network based on cell space distribution, and constructing a cell communication network by using a ligand-receptor database; (3) carrying out feature fusion and deep learning training by adopting GAT, extracting high-dimensional features, and designing a comprehensive loss function fusing a mean square error and triple loss based on MNN at the same time; and (4) globally optimizing rotation, translation and X-axis overturning parameters through a differential evolution algorithm, realizing precise alignment of the slices, and evaluating an alignment effect by a consistency index.

The invention relates to the technical field of image processing, and discloses a three-dimensional neural signal extraction method based on a four-dimensional light field, and the method comprises the following steps: S1, obtaining time sequence multi-view in-vivo neural data D under the four-dimensional light field; s2, analyzing neuron distribution according to the time sequence multi-view in-vivo neural data D, and obtaining multi-view data representing neuron positions; s3, reconstructing the multi-view data based on PSF to obtain three-dimensional space position data of neuron distribution; s4, performing neuron segmentation based on the three-dimensional space position data to obtain a segmentation mask body of each neuron; s5, performing full-frame-rate frame-by-frame reconstruction on the time sequence multi-view living neural data D based on PSF to obtain a time sequence three-dimensional body stack, and obtaining a neuron activity matrix of each neuron in the time sequence three-dimensional body stack according to the segmentation mask body; according to the invention, accurate capture and segmentation of neurons can be realized from the neural data of the four-dimensional light field, so that extraction of neuron activities is realized more accurately.

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 invention discloses a microbial target detection method based on an improved YOLOv8s network in a microscopic scene. The method comprises the following steps: constructing a target image data set; preprocessing the target image data set; extracting image features; fusing image features; and predicting the image. The microbial target detection method based on the improved YOLOv8s network comprises the step of replacing a YOLOv8s backbone network with SMA-YOLOv8s. In an SMA-YOLOv8s network, in a feature extraction part, a space-to-depth conversion mode is adopted to realize lossless downsampling of a low-contrast microbial image and complete feature extraction among different scales, a multi-scale feature fusion module based on a cascade mode is designed at a neck part, and a dynamic weight optimization regression branch loss function is used in a detection head. Therefore, the improved network can effectively extract the fine-grained features of the low-contrast microbiological image, and the capability of representing the context information of an occluded target is enhanced. The recognition and positioning capability of the microbial target in the microscopic scene is enhanced, and compared with a traditional algorithm, the universality is better.

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 present disclosure relates to a method and system for multi-source algae image target detection, and relates to the field of monitoring of algal bloom events in fresh water. The method includes first crawling images of algae of a selected species by using a built automated algae crawling tool, where the images include all formats; classifying and labeling algae in the algae images, and forming a source domain dataset by using all the classified and labeled algae images; performing transfer learning by using a faster recurrent revolutional neural network (Faster RCNN) with reference to a target domain dataset, to obtain a multi-source algae image target detection model; and finally performing identification and classification by using the multi-source algae image target detection model.

Disclosed herein, inter alia, are methods and systems of image analysis useful for identifying and / or quantifying features associated with biomolecules.

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 technical field of agricultural harvesting machinery, in particular to a low-damage mechanical method for synchronously harvesting peanut seedlings and fruits, which comprises the following steps: step 1, dynamically regulating and controlling the clamping force of seedling vines; 2, soil vibration parameter cooperative execution, wherein a vibration parameter combination is matched according to soil physical characteristics, and a vibration shovel is controlled to apply a composite vibration waveform; 3, seedling and fruit differential separation control, wherein the speed ratio of an upper-layer conveying belt to a lower-layer roller of a separation mechanism is set to be larger than 2, and an adjustable buffering check curtain is arranged above the lower-layer roller to control the falling height of fruit pods; 4, damage risk closed-loop correction, wherein the clamping pressure set value and the vibration frequency are synchronously and reversely adjusted according to the fruit drop rate detection result. Various key parameters are dynamically adjusted to accurately adapt to the change of an operation environment, so that the problems of vine clamping, soil adhesion, capsule collision damage and the like are effectively solved, and the peanut harvesting efficiency and the fruit quality are remarkably improved.

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.

Systems and methods for capturing and processing image data captured by an optical microscope, such as an Open Top Light Sheet (OTLS) microscope are disclosed. Image data can be processed by techniques including flat fielding, image depth correction, and edge correction, and pixel classification and image segmentation techniques can be applied to more accurately identify lipid droplets and fibrous structures for assessment of, for example, steatosis and fibrosis in human liver biopsy tissue samples. Systems and methods are also related to capturing low resolution images of a sample, identifying regions of interest, and then capturing high resolution image of the regions of interest to reduce memory used for storage, increase computation speed, and reduce computational power.

Burden on a user regarding counting colonies is mitigated. A colony counting device includes an acquisition section that acquires an image of colonies generated in a test individual, and an execution section that executes first software, the execution section executing processing of counting the number of the colonies from the image of the colonies. The execution section reads a user file that holds information in a matrix format is created by second software different from the first software in order to count the colonies generated in the test individual or to manage a count result of the colonies. Moreover, the execution section creates a count table to be used to count the colonies based on the user file.

The invention discloses an intelligent detection and analysis system for biological evaluation data of a medical instrument, and relates to the technical field of data processing, and the system comprises an image acquisition module which carries out the rapid imaging of the initial physical planking state of cells in a microwell plate; the image processing and feature quantification module is used for identifying the cell object and extracting a plurality of physical operation quality (POQ) feature parameters representing a population space distribution mode of the cell object; the quality evaluation and early warning module is used for comparing the POQ fingerprint with a preset quality baseline in real time and generating an early warning signal when the deviation exceeds a threshold value; and the data management and trend analysis module is used for continuously recording and analyzing POQ fingerprint historical data associated with the automatic pipetting equipment. According to the method, the physical operation quality is monitored at the experiment starting point, so that the accuracy, the reliability and the experiment efficiency of biological evaluation data of medical instruments are improved, the resource waste and the failure risk caused by initial inoculation errors are effectively reduced, and a way is provided for intelligent management of automatic equipment.

The invention provides a method for quickly analyzing TEM (Transmission Electron Microscope) images in batches and measuring critical dimensions by nanoscope software, which relates to the technical field of chip manufacturing and comprises the following steps of: constructing a TEM image intelligent analysis and measurement system; the TEM image intelligent analysis and measurement system comprises an image acquisition module, an image preprocessing module, a measurement template generation module, a measurement path planning module, a data fusion and analysis module and a cloud optimization module, and TEM-Nanoscope in the TEM image intelligent analysis and measurement system comprises a quantum calculation acceleration module (20 quantum bits and 10 CNOT gates) and an A * algorithm path planning module. The feature extraction time of the million-level TEM image is shortened from the hour level to the minute level (the efficiency is improved by 50 times), the path planning time is shortened to 1 second, and the bottleneck of slow processing speed of traditional manual analysis and mainstream software is solved. Compared with one-by-one treatment of DigialMicrograph and ImageJ, the method disclosed by the invention has the advantages that high-throughput analysis is supported, and the production efficiency is greatly improved.