192 results about "Cell segmentation" patented technology

The invention discloses a low-cost cell segmentation method, system and device based on active learning. The method comprises the following steps: selecting an initial cell image for preprocessing and bounding box labeling; forming a training set and constructing a target detection model for training so as to predict a bounding box of pictures of the verification set; taking the bounding box as a prompt for segmenting a cut model (SAM), and outputting a corresponding cell mask; adopting a Monte Carlo discarding method to predict the uncertainty of the unlabeled pictures, and selecting the pictures with higher uncertainty to be labeled by experts; adding the marked image data into the training set of the last round for updating, and repeating the training and sample selection of the model; and outputting a model weight and an index record after the required performance is achieved. According to the method, the data annotation cost of a cell segmentation algorithm is greatly reduced on the premise of ensuring the model performance.

The invention relates to a single cell image segmentation method based on a minimum circumcircle, which solves the technical problem of how to improve the precision, robustness and adaptability of an image-based single cell segmentation method, and comprises the following steps of: firstly, obtaining an original cell image, and secondly, converting the original cell image into a gray level image; preprocessing and binarization processing are carried out on the grayscale image to obtain a binarized image, a cell edge contour image is obtained through processing, a minimum circumcircle is drawn for each contour in the cell edge contour image to obtain a cell circumcircle image, and finally, the center of the minimum circumcircle is used as the center point of a cutting area to obtain a cell edge contour image. And determining the boundary of the cutting area by taking the background including the cells and within a certain range around the cells as a standard, cutting the cell circumcircle image, and finally obtaining a single cell image. The method is suitable for single cell identification and segmentation in a microscope image, and can be widely applied to the fields of cell biology research, medical diagnosis, drug screening and the like.

The present application provides methods and systems for image-based cell segmentation detection, image-based cell division detection, and image based cell tracking comprising receiving image data and applying machine learning algorithms to extract cell segments, division events, and cell tracks through images.

The invention provides a microscopic image representation method based on a dynamic pluggable mask self-supervision encoder. The microscopic image representation method comprises the following steps: step 1, establishing a network based on the dynamic pluggable mask self-supervision encoder and an MAE decoder; 2, constructing a loss function to train the network, and carrying out the combined optimization of image reconstruction and classification; step 3, using a pre-training dynamic pluggable mask self-supervision encoder to extract deep feature representation of the microscopic image; and further connecting a decoder of a downstream task, and carrying out mineral microscopic image super-resolution reconstruction, inclusion automatic analysis, intelligent diamond cleanliness rating and general cell segmentation. According to the method, the feature extraction quality of the field with the data volume disadvantage is improved through the pluggable module, and the problem that most categories of cross-field data sets are unbalanced is effectively solved. Meanwhile, compared with the addition of branches, the pluggable module reduces the extra calculation overhead brought by the addition of a structure by 50%, and endows the model with extremely strong domain mobility.

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

According to the microscopic cell image tracking method and device based on the graph neural network, the electronic equipment and the storage medium provided by the invention, a training set cell image is acquired and a training label is marked, a cell segmentation model is trained according to the training set cell image, and other data sets are predicted according to the cell segmentation model; according to the tracking method provided by the invention, the dependence on manual design can be reduced, the feature expression ability can be improved, the relationship between the cells can be modeled through the graph structure, the tracking precision under the condition of dense and shielded cells can be improved, and the tracking accuracy can be improved. Meanwhile, in combination with time sequence data, the time continuity of cell movement is captured, and the tracking stability is improved.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

The invention provides a method and a system for processing a cell image based on a DistSegNet model. The method for detecting the cells based on the DistSegNet model image processing technology comprises the following steps: preprocessing a collected cell image; performing cell segmentation on the cell image based on a DistSegNet model image processing technology to generate a cell nucleus region; based on the segmented cell nucleus region, generating a background region, a foreground region and an edge region by adopting morphological operation; calculating the shortest Euclidean distance from each background region pixel to the nearest foreground region pixel, and segmenting the cell image by using a watershed algorithm; performing result optimization on the segmentation map corresponding to each cell instance label to obtain a filtered segmentation label map; and extracting the outer contour of the intra-nucleus region and the outer contour of the surrounding region of each cell from the segmentation mark graph, and storing the outer contours to a corresponding result file. According to the invention, the accuracy and accuracy of a WSI-level cell detection result can be improved.

The invention discloses an intelligent bone marrow megakaryocyte morphology detection method which comprises the following steps: S1, data preparation: collecting and preprocessing a digital large map of a bone marrow smear, labeling megakaryocytes, and establishing a labeled sample; s2, generating and sorting a sample: extracting a small graph sample by taking megakaryocyte as a center; s3, constructing a deep learning model: constructing and training a deep convolutional neural network, and performing model optimization and performance improvement by using the generated small image sample set; s4, large image detection and reasoning: calling the trained model for reasoning by adopting a sliding window mechanism, and fusing detection results of a plurality of small windows back to an original large image through confidence weighting and a non-maximum suppression strategy; and S5, target cell segmentation: carrying out target segmentation on the detected megakaryocyte, and introducing a pyramid structure for cells with different sizes to obtain an accurate segmentation mask of each target cell. According to the method, the megakaryocyte detection and segmentation precision is improved through large image labeling, small image training and a sliding window reasoning strategy.

The invention relates to a cervical TCT image cell segmentation method based on improved U-Net, and belongs to the field of image processing technology and medical technology. The problems that complex morphological characteristics of cells are not fully considered and fine cell segmentation is insufficient in cervical cancer intelligent screening research based on cervical TCT images are solved. Comprising the following steps: preprocessing a cervical cell TCT image; constructing a segmentation network based on the EGE-UNet; evaluating the cell image segmentation model based on qualitative and quantitative evaluation; and obtaining a cervical cell segmentation result based on the evaluated model. According to the method, high-quality cervical TCT cell image segmentation and fine segmentation of cervical cell nucleuses and cytoplasm can be realized by using the EGE-UNet model, and related research of subsequent cervical cell intelligent diagnosis is facilitated; based on an accurate cell segmentation result, the method can assist in extracting various cell morphological characteristics, has clinical guiding significance, and provides more interpretable quantitative data for subsequent cervical cell research.

The invention discloses a Transform-based end-to-end cell segmentation and tracking method and system, and the method comprises the steps: employing an improved model based on a Mask DINO model to achieve cell segmentation and tracking, and employing a top-K query selection strategy to select a K feature point with the highest probability from an output feature map for object query, taking the track from the previous frame t-1 as a tracking query in the current frame t; additionally generating a split query representing mitosis; decoding the object query, the tracking query and the split query by using a Transform decoder; and performing prediction by using a classification head to obtain a classification label, a rotating bounding box and a segmentation mask of a cell in the image I of the current frame t. The invention aims to solve challenges in a cell segmentation and tracking scene and improve the accuracy of cell segmentation and tracking.

The invention discloses a stem cell fusion degree detection method and system based on artificial intelligence, and a storage medium. The method comprises the following steps: carrying out image preprocessing on a cell microscope image; automatically calculating an optimal threshold value by using an image threshold value segmentation algorithm to obtain a cytoplasm mask; median filtering is carried out on the original image to reduce noise, then an adaptive threshold segmentation method is adopted, a local threshold is calculated according to local area gray level distribution, and a cell nucleus binary image is generated; performing connected region marking on the cell nucleus binary image, calculating the area attribute of each region, and performing filtering according to a cell nucleus removal ratio parameter to obtain a cell nucleus mask; performing logic OR operation on the cytoplasm mask and the cell nucleus mask to obtain a complete cell segmentation result; and calculating the fusion degree of the stem cells based on the cell segmentation result. Therefore, the problems of accuracy and consistency of judging the fusion degree of the stem cells by observing microscope images with human eyes in the prior art are solved, and accurate detection of the fusion degree of the stem cells is realized.

The invention provides a signal point identification method and a related product. According to a specific embodiment of the method, instance segmentation is performed on a cell nucleus image based on a cell instance segmentation model, and a cell segmentation result of each cell in the cell nucleus image is obtained; based on a fusion feature extraction model, carrying out image feature extraction on the fluorescence signal point image of each channel to obtain a fusion feature map; then, based on the cell segmentation result of each cell, extracting a cell region fusion feature map of each cell from the fusion feature map; then, based on a cell region mask feature extraction model, obtaining a masked cell region fusion feature map of each cell; and finally, based on a signal point identification model, performing signal point identification on the masked cell region fusion feature map of each cell to obtain a signal point identification result of the corresponding cell in each channel. Therefore, end-to-end cell segmentation and signal point identification can be realized, cell segmentation and signal point identification can be realized based on the cell nucleus image and the multi-channel fluorescence signal point image, cell segmentation and signal point identification can be completed at the same time with fewer model parameters, the calculation amount is saved, and the algorithm processing time is accelerated.

The invention relates to the technical field of crop monitoring, in particular to an automatic cell segmentation method for a crop remote sensing image based on geographic prior information. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; performing semi-automatic segmentation on an unplanted bare soil region and a region of crops with unknown growth vigor; aiming at a crop area with known growth vigor, carrying out batch full-automatic segmentation processing on the multi-source sensor image by adopting an improved Ground-SAM segmentation large model, and outputting a segmentation result; and carrying out optimization processing on the segmentation result, and finally outputting an optimized cell segmentation map of the multi-source crop remote sensing image. The method has the advantages of accurate registration, efficient segmentation, multi-sensor compatibility and automatic processing, and remote sensing monitoring is improved.

The invention relates to the field of image processors and biological medicines, and discloses an NK cell activity rapid detection method based on image processing. The method comprises the following steps: acquiring an unmarked time sequence phase image sequence of an NK cell and target cell co-culture system; performing cell instance segmentation to track individual cells; extracting a morphological dynamic characteristic parameter set of the target cell, wherein the morphological dynamic characteristic parameter set comprises a volume change rate, a phase gradient entropy, a cytoplasm phase fluctuation frequency and a nuclear region phase mean value; inputting the parameters into a pre-trained death state discrimination model, and outputting a death probability; and calculating a killing efficiency index based on the death probability evolution curve, and judging the activity level of the NK cells. The system comprises a phase image acquisition unit, a cell segmentation unit, a feature extraction unit, a death judgment unit and an activity judgment unit. Through unmarked imaging and deep learning fusion analysis, high-precision, real-time, quantitative and ultra-early NK cell activity evaluation is realized, and the method is suitable for clinical instant inspection and immunotherapy monitoring.

The invention provides a medical image cell segmentation method based on progressive pseudo-label optimization in order to solve the problem of learning deviation caused by unreliable pseudo-label learning and the limitation that a fixed-form pseudo-label cannot provide reliable cell morphological characteristics in an existing weak supervision method. The method comprises the following specific steps: 1) utilizing weak supervision point labeling information, generating two initial pseudo labels with complementarity through a clustering algorithm and a superpixel segmentation algorithm, and respectively guiding a training process of a double-branch network; 2) utilizing a dynamic threshold and watershed algorithm to realize growth and boundary division of a cell region in the pseudo tag, so that the pseudo tag is gradually close to the real form of a cell; 3) designing a bidirectional cross supervision mechanism, and realizing knowledge migration and collaborative optimization through high-confidence prediction results of the two branch networks; and 4) uncertainty estimation and a difficult sample attention loss function are designed, the feature learning ability of the network to difficult samples with low contrast, fuzzy boundary and the like is enhanced, and the precision of weak supervision cell segmentation is effectively improved.

The invention discloses a morphological feature-based cell segmentation and cell nucleus identification method for a turned unstained bone tissue pathological image. The method comprises the following steps of: 1, eliminating tool marks by adopting a tool mark elimination method combining local frequency domain analysis and directional suppression; 2, performing cell segmentation by using a K-means method, and performing morphological expansion and topological analysis on a segmented single cell image to identify a cell nucleus in the single cell image; step 3, calculating morphological characteristic indexes of each region; the method comprises the following steps: establishing a multi-dimensional Gaussian mixture model according to existing bone cell labeled sample information, performing outlier detection according to statistical data analysis, and removing results which do not conform to cell morphology; classifying different regions, and removing non-cell regions; by calculating morphological characteristic indexes of each region, different regions are distinguished according to the indexes, and cells are preliminarily screened. According to the method, high-precision cell segmentation and cell nucleus identification can be carried out on the cut undyed bone tissue pathological image.

The invention provides a tumor microenvironment cell spatial distribution characteristic quantitative analysis method and system, and the method comprises the steps: obtaining a pathological section image, and obtaining cell segmentation and classification information in the pathological section image through a pre-trained cell recognition model; obtaining cell space distribution information based on the cell segmentation and classification information; constructing a cell topology network according to the cell space distribution information, and obtaining a first cell space distribution characteristic; constructing a spatial weight matrix, and obtaining a second cell spatial distribution feature; and judging a tumor state by combining the first cell spatial distribution characteristic and the second cell spatial distribution characteristic so as to realize typing of the target object. The method has good cell segmentation and classification precision, fully pays attention to the adjacency relation, the action intensity and the space structure between cells, integrates multi-dimensional space distribution characteristic quantitative analysis, realizes a good tumor microenvironment cell analysis result, is used for accurately realizing the typing of a target object, and has good universality.

The invention provides a pathological image processing method and system, and the method comprises the steps: carrying out the color space decoupling of an original pathological image, separating a plurality of staining components corresponding to the optical absorption characteristics of a staining agent, carrying out the space alignment and channel superposition of the staining components, and generating a multi-channel structure diagram containing the structural characteristics of cells; inputting the multi-channel structure diagram into an adversarial network for color distribution correction to obtain a standardized red-green-blue three-primary color image; performing morphological optimization on the standardized red-green-blue three-primary color image by using an optimization process to obtain a mask, and performing Euclidean distance transformation on the mask to generate a distance map; performing channel splicing on the mask, the distance map and the standardized red-green-blue three-primary-color image, inputting a multi-task deep learning network, and outputting a probability thermodynamic diagram and a boundary probability diagram of a cell mass center; and cell segmentation is realized based on the probability thermodynamic diagram and the boundary probability diagram by utilizing a segmentation process, so that the problem of structural deformation of the image is effectively avoided.

The invention discloses a spatial omics single cell data acquisition method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a dyeing image of a biological sample, and acquiring a gene expression visual image of the biological sample; carrying out registration on the dyed image and the gene expression visualization image to obtain a registered image; performing cell segmentation on the registered image to obtain a cell mask pattern; determining cells of background gene molecules of unknown cells in the gene expression visualized image according to the cell mask graph, and marking the cells to obtain a space group molecular expression matrix with cell markers. Compared with the prior art, the method has the advantages that high-precision spatial single cell data is obtained through the processing flows of gene expression matrix graph, splicing, registration, segmentation and correction.

The invention relates to an unsupervised cell segmentation method based on Transform, and the method is characterized in that a multi-modal text image alignment module aims at effectively fusing text and image data, and achieves the high alignment of multi-modal information through a Transform architecture; the mutual relevance of the data is enhanced through a low-rank attention mechanism, so that the multi-modal features can be extracted and aligned more accurately in an unsupervised environment. The matching matrix feature optimization module further processes the aligned feature data. According to the method, a unique matching matrix optimization algorithm is utilized, the precision of feature matching is remarkably improved, parameters of segmented cells are extracted and adjusted through the optimized matching matrix, and a more accurate initial prompt is provided for the subsequent segmentation process. The optimized features are input to an SAM segmentation module. And the SAM realizes high-precision cell segmentation by utilizing the strong segmentation capability of the SAM. The module gives full play to the advantages of a low-rank attention mechanism and matching matrix optimization, and ensures the accuracy and robustness of a segmentation result.

The invention discloses a mammary gland immunohistochemical Ki-67 image segmentation method based on an attention mechanism, and the method comprises the following steps: obtaining a mammary gland immunohistochemical Ki-67 image, and carrying out the segmentation processing of the mammary gland immunohistochemical Ki-67 image, and obtaining mammary gland immunohistochemical Ki-67 image blocks; taking the breast immunohistochemical Ki-67 image block as input, taking the corresponding cell type classification result as output, and training a pre-constructed breast cell segmentation model; a mammary gland cell segmentation model is constructed based on a VGG16 network and a cooperative attention mechanism of a space and a channel; segmenting a mammary gland immunohistochemical Ki-67 image to be predicted into a plurality of image blocks, and inputting the image blocks into the trained mammary gland cell segmentation model for segmentation; and obtaining a distribution density map of classification results of different types of cells. The invention relates to the technical field of image segmentation, and by coordinating the relationship between spatial features and channel features and enhancing the guiding capability of the features, semantic feature differences are effectively relieved, and the precision, flexibility and adaptability of tumor cell segmentation are remarkably improved.

Systems and methods for gradient-to-parameter ratio guided feature alignment for model adaptation. To adapt an artificial intelligence (AI) model to different domains, activation statistics for the AI model can be computed from collected domain data. Weights of the AI model can be adjusted based on the activation statistics of the training gradients. The AI model can be fine-tuned by focusing adaptation intensity to layers with attention mechanism by using a ratio of gradient norm over parameter norm to obtain a fine-tuned AI model. The fine-tuned AI model can be employed to perform downstream tasks such as cell segmentation from medical images.

The invention belongs to the technical field of breast cancer treatment, and particularly relates to a patient layering method based on a breast cancer single cell space proteome multicellular enrichment mode, which comprises the following steps: S1, breast cancer single cell space proteome image data cell segmentation and cell type identification; s2, constructing a cell network diagram, and simulating a tumor microenvironment; s3, detecting closely connected cell communities, and extracting cell community characteristics; s4, carrying out cell community feature clustering, and representing a tumor microenvironment distribution mode of the patient; and S5, calculating the similarity of the tumor microenvironment distribution modes of the patients, and layering the patients. By means of the method, the patients can be layered, and prognosis of the patient groups has significant difference.

The invention discloses an automatic segmentation and counting method and system for fundus cells, and the method comprises the steps: preprocessing a fundus cell image, inputting the preprocessed fundus cell image into a deep learning model, and enabling a lightweight coupling segmentation head embedded with an attention mechanism module to be introduced into a YOLOv8-seg cell segmentation model, and carrying out the training, thereby obtaining the deep learning model; the attention mechanism module divides the feature map into G sub-features and sends the G sub-features to two parallel branches, one generates a weighted feature map P1 * 1, the other extracts a feature map P3 * 3, 2D global average pooling is used for global space coding of the P3 * 3 and the P1 * 1, and weights w3 * 3 and w1 * 1 are generated; matrix multiplication is carried out on the feature map and the weight, a cross-space interaction weight is generated, and the weight is multiplied by x element by element to obtain Y which belongs to RC * H * W; according to the method, the RGCs in the fundus model can be automatically and accurately circled.

Biological landmark segmentation may comprise disambiguating which spatial transcriptomics data is associated with background noise and / or partitioning distinct biological landmark types, biological landmarks, and / or biological landmark features. An iterative process may be used to determine such a segmentation, the iterative process comprising modeling gene expression rates according to a current state of the segmentation; randomly altering a current state of the segmentation; determining an updated model of the gene expression rate based on the alteration; and determining to accept or reject the alteration based on a likelihood determined based on the updated model.

The invention provides an automatic cell detection method and device of a fluorescence in-situ hybridization image and a readable storage medium of the automatic cell detection method and device. According to the method, the fluorescence signal classification detection capability of Yolov12 and the fine boundary segmentation advantage of Cellpose are fused; through data preparation and labeling, double-model targeted training, segmentation prediction, post-processing optimization and mask logic and operation, collaborative decision is realized, and the problems of difficult cell overlapping segmentation, fluorescence signal attenuation interference, strong subjectivity of manual interpretation and the like in existing FISH image analysis are solved. The method can significantly improve the segmentation precision and the edge recognition capability of the overlapped cells, achieves full-automatic efficient processing, provides an objective and reliable basis for the diagnosis of related diseases, and has high precision, high efficiency and strong objectivity.

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

The invention discloses an image processing method and system based on an in-situ hybridization technology and a medium, and relates to the technical field of biological information, and the method comprises the following steps: converting a cell DAPI dyeing result into two-dimensional data from three-dimensional data by utilizing Z-axis maximum intensity projection, identifying and separating a single cell from the two-dimensional data by utilizing cell segmentation, and when the cell segmentation is used for processing an overlapping region, identifying and separating the single cell from the two-dimensional data. Using a registration algorithm Ashlar to calculate an error between adjacent visual fields of the same round to obtain cell position information; an imaging result after hybridization of the fluorescent probe and the gene is subjected to multiple rounds of registration through a registration algorithm Ashlar to generate a spliced image, local maximum values of the spliced image under different rounds are marked by using a fluorescent dot recognition algorithm, so that position information of each fluorescent dot is obtained, the position information of each fluorescent dot corresponds to the gene in a transcript, and the position information of each fluorescent dot corresponds to the gene in the transcript. Obtaining gene position information; distributing genes into cells by utilizing the cell position information and the gene position information to obtain a cell gene matrix and displaying the cell gene matrix; according to the image processing method and system and the medium, accurate matching of sequencing data and spatial information is achieved, and then the distribution rule of gene expression in space is better revealed.

In embodiments, a method includes reading a nuclear segmentation mask of an image including pixels arranged in two dimensions. The nuclear segmentation mask identifies cellular nuclei stained with a cellular nucleus stain. The method includes determining amplitudes for each pixel. Each amplitude corresponds to exactly one dimension of the two dimensions. The method includes constructing a graph having nodes and edges. Each node corresponds to a pixel. Nodes corresponding to neighboring pixels are connected by an edge. The method includes assigning a weight to each edge. Each edge's weight is based on amplitudes of pixels corresponding to nodes connected thereby. The method further includes, based on the graph, determining, for each cellular nuclei, a heat map corresponding to a predicted cell region associated with that cellular nucleus. The method further includes based on the heat maps of the cellular nuclei, determining a cellular segmentation mask comprising predicted cell regions.