1167results about "Microscopic object acquisition" patented technology

A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled / label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

The invention relates to the technical field of image recognition, in particular to an online real-time image recognition anti-counterfeiting verification method and system based on gold transaction. The method comprises the following steps: acquiring a micro texture image of the gold product; generating a unique digital fingerprint based on the physical unclonable feature of the microscopic texture image, and performing encryption in combination with a UTC timestamp to generate a dynamic encryption tag; extracting a feature descriptor of the micro texture image and performing texture comparison with a pre-stored database to obtain a texture matching degree; decrypting the dynamic encryption label to generate a dynamic code aging state; and according to the texture matching degree and the dynamic code aging state, performing anti-counterfeiting discrimination on the micro texture image of the gold product to obtain a genuine product discrimination result and a counterfeit discrimination result. According to the method, the micro texture image, the near infrared spectrum data, the dynamic encryption label and the block chain technology are combined, so that the accuracy, the safety and the efficiency of gold anti-counterfeiting are improved.

A method for automated, artificial-intelligence-based COC retrieval includes using an artificial intelligence / machine learning system (AI / ML system) to optically scan a follicular fluid sample to produce an image object using an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes comparing the image object to a predetermined threshold using an AI / ML system, wherein the predetermined threshold is at least in part an optical pattern with a probability of corresponding to a cumulus-oocyte-complex (COC). The method includes identifying a COC within the follicular fluid sample based at least in part on the image object satisfying the predetermined threshold. The method includes determining a COC location within the follicular fluid sample based at least in part on identifying a region of the image object corresponding to an optical pattern within the image object that satisfies the predetermined threshold.

An apparatus and method for detecting content of interest on a slide using machine learning. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a first image, comprising a macro image, identify areas of interest associated with the grids of the first image, receive a second image comprising a high magnification image associated with the areas of interest of the first image, classify, using at least a probed point, the grids of the first image, wherein classifying the grids of the first image includes classifying the grids into accepted grids of the grids and rejected grids of the grids, scan, using the image capturing device, the accepted grids to generate an output image, and display, using a display device, the output image.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

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 composite fiber membrane aperture distribution prediction method based on deep learning, and relates to the technical field of distribution prediction.The method comprises the steps that multiple kinds of image data are collected and coded respectively, and a composite coding feature vector is generated through an attention mechanism; based on the composite coding feature vector, performing region segmentation on a fiber cross-linked region and a non-cross-linked region, and extracting and quantifying pore size distribution dominant features; performing associated coding by combining the pore size distribution dominant characteristics and spinning process parameters; and based on the associated codes, optimizing the depth prediction network, and adjusting the parameter weights of the associated codes by adopting an error correction mechanism. The pore size distribution of the composite fiber membrane can be accurately predicted, and powerful support is provided for material design optimization and production process control.

The invention includes systems and methods to combine acoustic sorting, high-throughput imaging technology and machine learning, such as convolutional neural networks (ConvNet) analysis, to analyze cells, pathogens, and other target particles from biological samples resolvable by high-throughput imaging microscopy, or other comparable instrument.

The invention relates to the technical field of medical image processing, in particular to an AI-based cervical cell image analysis auxiliary method and system, and the method comprises the steps: carrying out the multi-modal data collection, including cervical cell image data, clinical data and patient gene data, and carrying out the preprocessing of the collected cervical cell image data; constructing a cervix uteri cell image generative adversarial network to perform image enhancement and feature extraction on the cervix uteri cell image; fusing the extracted features with clinical data and patient gene data, and training a cervical cell classification model by using the fused feature data; and inputting a real cervical cell image into the trained cervical cell classification model, and outputting whether the cervical cells are abnormal or not and an analysis result of the abnormal type and degree. According to the method, multi-modal data acquisition is combined with an image enhancement and classification method based on a deep learning technology, so that the efficiency of cervical cell image analysis is improved, and the workload of doctors is reduced.

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 petroleum pipe microscopic fracture morphology identification method, system and device and a medium, and belongs to the technical field of failure analysis image process.The method comprises the steps that a deep learning classification network with a residual module is constructed and trained, and a deep learning classification network loaded with a network connection weight matrix is obtained; and finally, the deep learning classification network is utilized to efficiently and accurately identify the to-be-tested object region image. According to the method, massive data sets can be quickly processed and analyzed by using the high-speed processing capability of a modern computer, so that the efficiency is greatly improved. Compared with a traditional method, the automatic fracture classification method can reduce subjective interference of people and enhance reliability and consistency of results. And meanwhile, the identification method is simple, engineering application integration is facilitated, and good practicability and expansibility are achieved.

The invention discloses a microscopic image scanning and parallel efficient splicing method based on a spanning tree, which considers the characteristic that the scanning visual angle of a microscope is distortionless and the characteristic that the corresponding relation between the physical scale and the pixel scale of a microscope platform is unchanged, and has the advantages of an image matching algorithm based on a template. Automatically dividing an overlapping region range for a template matching algorithm, fully considering the matching result credibility of the pictures on the surrounding adjacent sides, and calculating a global splicing point location by using a spanning tree algorithm; picture splicing types are divided through rows and columns, the problem of memory access conflicts is avoided, the computing power improvement brought by multiple threads is fully utilized, and the picture splicing efficiency is improved. According to the method, adjacent image matching, high-precision matching of global splicing point locations based on the spanning tree and a multi-thread high-speed image splicing technology are realized.

The invention discloses a cement microstructure design method based on a neural network and multi-scale feature constraint, and the method comprises the steps: obtaining a two-dimensional image of a cement microstructure, and building a cement microstructure data set; obtaining a hyper-parameter group of a random step number according to a predetermined constraint scale number, and training a neural network model according to a constraint rule and the hyper-parameter group to obtain a microstructure feature reconstruction operator; establishing a reconstruction algorithm according to the constraint rule, testing the neural network model, and selecting and storing the neural network model conforming to a preset model; and using a reconstruction algorithm and a microstructure feature reconstruction operator to reconstruct the cement microstructure through a predetermined constraint scale number. According to the method, the reconstruction algorithm and the microstructure feature reconstruction operator are used, the cement microstructure is reconstructed, flexible customization of phase distribution in the cement microstructure is supported, the technical barrier of a neural network method in initial state synthesis of a cement hydration model is broken, the time and labor cost of the cement manufacturing industry is remarkably saved, and carbon emission is reduced.

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.

The invention discloses an intelligent management system and method for efficient utilization of straw feed resources, particularly relates to the technical field of production management, and is used for solving the problem that accurate recognition and dynamic management and control of local structure abnormal phenomena are lacked in the existing straw pretreatment process. Fiber component distribution information is generated through spectral component analysis, a multiple linear regression algorithm is combined to identify a local structure easy-curing risk area, microscopic image acquisition and feature extraction are used to identify a potential curing feature area, and a pre-processing risk adjustment area is generated based on the two areas. The abnormal degree of the risk area is evaluated by analyzing the deviation amplitude of fiber direction arrangement and the asymmetric coupling strength of interface interaction, and finally whether parameter optimization processing needs to be carried out or not is judged according to the evaluation result, so that dynamic monitoring and accurate regulation and control of the straw pretreatment process are achieved, and the straw pretreatment efficiency is improved. The stability of the pretreatment process and the utilization efficiency of straw feed resources are improved.

The invention discloses a portable sedimentary rock classification system and method based on deep learning, and relates to the technical field of rock identification and geological survey. The system comprises a portable microscopic image acquisition module, a cloud deep learning model and a mobile terminal module. A user shoots macroscopic or microscopic images of sedimentary rocks through a mobile phone, the macroscopic or microscopic images are uploaded to a cloud end and then subjected to feature extraction and classification through the optimized ResNet50 model, and a classification result is fed back to a user end in real time. The innovation points comprise: (1) adopting a macroscopic and microscopic data set training model to significantly improve the classification precision; (2) designing a portable microscopic module to realize rapid acquisition of microscopic images; (3) mobile terminal identification service; and (4) the ResNet50 model based on residual network optimization supports high-precision fine-grained classification (sandstone, conglomerate, siltstone and clay rock). Experiments show that the system classification accuracy reaches 99% or above, and compared with a traditional laboratory method, the efficiency is greatly improved. The method can be widely applied to geological exploration, teaching and scientific research and science popularization scenes.

The invention belongs to the technical field of food safety, and discloses a full-automatic fast detection method and system for multiple types of target objects based on food traceability. The method comprises: using a microscope to collect a food finished product image; analyzing and processing the food finished product image to obtain a first finished product image; analyzing and processing the first finished product image to obtain first feature data; performing component detection on the food finished product, and extracting a target detection component and a corresponding target detection component content; analyzing and processing the target detection component to obtain second feature data; obtaining an ingredient table corresponding to the finished food; according to the first feature data and the second feature data, constructing a food feature equation set, and according to an equation solution of the food feature equation set, judging whether the ingredient list is consistent with the raw materials actually used by the food finished product; according to the scheme, particle feature analysis and component detection are combined, the consistency of the ingredient table and the actual components is quantitatively verified, and the ingredient table is effectively prevented from being counterfeited.

The invention relates to a method implemented by a data processing apparatus, comprising the steps of receiving an image; providing a set-point for a desired image quality parameter of said image; and processing said image using an image analysis technique for determining a current image quality parameter of said image. In the method, the current image quality parameter is compared with said desired set-point. Based on said comparison, a modified image is generated by using an image modification technique. The generating comprises a step of deteriorating said image in terms of said image quality parameter in case said current image quality parameter exceeds said set-point. The modified image is then output and may be further analysed.

The invention discloses a method for discriminating chicken breast with a white line and wood symbiosis defect based on a hyperspectral microscopic imaging technology, which comprises the following steps: acquiring normal meat, white line meat, wood meat and white line and wood symbiosis chicken breast samples, and cutting the samples into microsections; building a hyperspectral microscopic imaging system, and collecting and correcting microscopic spectral data; extracting a region of interest by using an image processing algorithm; performing defect grade labeling on each region of interest; extracting texture features of the microscopic hyperspectral image of each region of interest; selecting the characteristic wavelength of the spectral data in each region of interest; the obtained spectral data under the characteristic wavelength of each region of interest is fused with image texture features to serve as the input of a model, the defect category and defect grade score of the corresponding region of interest serve as the output of the model, and a Transform model is established and trained; compared with the existing visual and palpation methods, the method provided by the invention has the advantages of high accuracy and high efficiency.

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.