158 results about "Cell segmentation" patented technology

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 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.

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 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 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 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 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.

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.

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 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.

The invention relates to the field of medical image processing, and particularly discloses a TLS structure sketching system and method based on artificial intelligence, and the method comprises the steps: S1, image preprocessing: carrying out the standardization processing of an input HE staining section image, firstly separating cell nucleus and cytoplasm staining components through a color deconvolution algorithm, and highlighting the nucleoplasm contrast of lymphocytes; then strengthening the cell contour boundary by adopting an edge detection algorithm, and connecting the fracture edge through morphological operation to form a continuous and clear cell boundary mask; s2, morphological feature extraction: performing single cell segmentation based on the preprocessed image, and extracting geometric features and texture features of each cell; through machine learning model training, distinguishing lymphocytes and non-lymphocytes according to the characteristics, and generating a lymphocyte distribution probability graph; and S3, preliminarily identifying the aggregated area. By adopting the technical scheme of the invention, the TLSs differentiation stage can be identified, and the identification accuracy can be improved by combining morphological characteristics and spatial distribution characteristics.

Systems and methods for multi-modal subcellular segmentation using photolysable biomarkers and / or transcriptomic readout density maps are disclosed. The systems and methods improve the accuracy of cell segmentation of the nucleus, cytoplasm, and cell membrane regions by using optical and bleach correction from a variety of photolysable morphological markers in combination with high quality 3D images acquired with high dynamic range scans and spatial transcriptomic readout density maps.

The invention belongs to the technical field of medical treatment, and particularly relates to a cancer prognosis marker screening method which comprises the following steps: step 1) acquiring an imaging mass spectrometry flow cytometry analysis data set; 2) performing single cell segmentation on the image in the imaging mass spectrometry flow cytometry analysis data set, and annotating the cell type of the single cell according to the expression intensity of the marker to be screened; 3) identifying an area where the epithelial cells, the immune cells and the fibrotic cells intersect, and obtaining marker expression intensity in the area where the epithelial cells, the immune cells and the fibrotic cells intersect; 4) obtaining an intercellular interaction analysis result based on the cell frequency and the marker expression intensity, and 5) performing regression analysis according to the cell frequency, the marker expression intensity and the intercellular interaction result in different comparison groups, and screening to obtain the prognostic marker.

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

This invention provides a method and system for macrophage morphology recognition that integrates multimodal data. The method includes acquiring a time-lapse imaging sequence of live macrophage cells; segmenting and tracking individual cells using a probabilistic model of cell contour evolution and intracellular texture flow to obtain motion trajectories and continuous morphological contour sequences; acquiring behavioral features based on the motion trajectory; obtaining morphological features based on the morphological contour sequences; calculating local field influence features based on the behavioral and morphological features of neighboring cells within a neighborhood search radius; constructing a cell interaction graph structure based on the three types of features combined with intercellular Euclidean distance, motion direction correlation, and morphological features of cells at both ends; inputting the cell interaction graph into a trained graph attention network; and determining whether each macrophage is of subtype M1 or M2 based on the output.

The invention discloses a pathological image data enhancement method based on guidance of a visual-language basic model, and the method comprises the steps: extracting pathological image features and corresponding text features through a pathological visual-language basic model; and performing multi-modal feature fusion by using a cross attention mechanism, and guiding a diffusion model to synthesize a high-quality pathological image matched with the label, thereby realizing data enhancement of a pathological diagnosis task. Comprising the following steps: constructing text description corresponding to a pathological image by using label information of a pathological image data set in combination with a big language visual assistant model for pathological pre-training and a cell segmentation basic model, so as to more accurately characterize a tissue type, morphological details and multi-level visual features of cell distribution of the pathological image; an image-text multi-modal feature fusion network module based on a cross attention mechanism is constructed, and a diffusion model is guided to synthesize a pathological image by taking fusion features as control conditions, so that the synthesized image is ensured to be more matched with data label information, and the scale of a pathological image data set is effectively enhanced.

The present invention relates to the field of image recognition and judgment technology, and in particular to a method for judging the estrous cycle of rats based on image recognition. Its technical solution includes 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. The present invention significantly improves the efficiency and accuracy of morphological recognition of rat vaginal exfoliated cells through systematic and precise operations. From precise sample collection and image acquisition optimization, to multi-dimensional feature extraction and integrated learning classification, and then to result verification and feedback, the scientific nature of recognition is fully guaranteed, which not only greatly shortens the detection time and reduces manual errors, but also continuously improves performance through dynamic optimization, providing efficient and reliable technical support for the study of the estrous cycle of rats.

The invention discloses a synchronous imaging method for a multicellular structure in a multicellular type. The synchronous imaging method comprises the following steps: S1, marking the cellular structure in the multicellular type by using fluorescent protein; s2, forming a spectrum-spatial feature joint coding set according to the spectrum and spatial features; s3, performing spectral unmixing on the spectral image to obtain an abundance image of each fluorescent component; s4, performing cell segmentation and cell structure positioning, and extracting spatial features; and S5, carrying out joint decoding on the spectral features and the spatial features to distinguish different cell types, and carrying out synchronous imaging on the multi-cell structure. The method has the beneficial effects that different cell types are marked by using corresponding fluorescent proteins, and a spectrum-space coupling coding strategy is formed in combination with spatial position characteristics, so that simultaneous observation and dynamic analysis of different cell types and a plurality of internal structures in the same imaging view field are realized; and the limitation that traditional spectral imaging can only work in a single cell type is broken through.

The invention relates to a spatial analysis single cell state modeling method and system based on domain self-adaption and layered fine tuning, and the method comprises the steps: obtaining multiple immunofluorescence images and single cell segmentation masks, and constructing a no-label data set; constructing a mask auto-encoder composed of a ViT encoder and a linear decoder, adding a classification token in front of the image, carrying out field adaptive training on the mask auto-encoder based on the unlabeled data set, learning the classification token, and obtaining a field adaptive weight of the ViT encoder; obtaining a labeled data set; constructing a state embedding generation model, wherein the state embedding generation model comprises a shared ViT backbone network and a two-stage classifier; a classification token is added in front of an image feature sequence in the labeled data set, hierarchical training is carried out on the state embedding generation model, and the classification token is learned; and inputting the cell image blocks into the trained state embedding generation model, outputting a classification result and cell state embedding, and carrying out interpretability analysis. Compared with the prior art, the method has the advantages that accurate cell classification can be realized, and cell state representation with biological interpretability can be generated.

The present invention discloses a method and device for automatically identifying and segmenting cell somas based on two-photon calcium imaging data. First, a two-photon calcium imaging video of cells is acquired, and image denoising and image enhancement are performed on each frame of the video image. Then, the two-photon calcium imaging video is reduced in dimension to a locally correlated summary image, and a cell recognition algorithm based on multi-scale dot enhancement is applied to the locally correlated summary image to calculate the central coordinates of near-circular structures of different sizes within the imaging region, i.e., the seed points of the cell somas. Finally, for each seed point, the two-photon calcium imaging video is cropped into a block video of a fixed size centered on the seed point, and a cell segmentation algorithm based on an elliptical shape-constrained active contour model is applied to the block video to obtain the contour of the cell soma. The present invention can improve the operation efficiency and reduce the data storage volume.

The invention provides a cell interpretation method and device based on uncertainty evaluation, and the method comprises the steps: carrying out the cell segmentation and feature extraction of a sample image of a target sample, and determining a plurality of to-be-interpreted cells in the sample image and the feature information of each to-be-interpreted cell; respectively inputting each piece of feature information into a fusion interpretation model comprising a plurality of interpretation sub-models to obtain a prediction probability value and uncertainty measurement output by each interpretation sub-model so as to predict the probability that each to-be-interpreted cell is a target cell and evaluate the uncertainty of the probability; and determining a score value of each to-be-interpreted cell by a fusion layer in the fusion interpretation model based on the prediction probability value and the uncertainty measurement, and interpreting whether each to-be-interpreted cell is a target cell based on the score value so as to determine a cell interpretation result of the target sample. Through the method, the stability, the accuracy and the efficiency of interpreting the cells are improved.

A spatial in situ sequencing method, belonging to the field of biology, is proposed. It utilizes an electric field-assisted directed migration of mRNA and in-situ capture with primers on a microarray surface to enrich tissue and release mRNA. In-situ reverse transcription generates covalently fixed cDNA, ensuring high positional stability during multiple rounds of hybridization and imaging. After reverse transcription, tissue is digested to remove tissue, reducing spatial hindrance and background interference, while the cDNA remains at its original coordinates due to covalent anchoring. Combining coding probe hybridization and RCA, single-molecule-level signal amplification and recognition are achieved. Through decoding and single-cell segmentation, transcripts are mapped to their respective cells, constructing a single-cell resolution spatial gene expression map. This method does not rely on multi-round DAPI mapping or other endogenous morphological marker-based multi-cycle image registration methods, making it suitable for high-throughput spatial in situ sequencing and significantly improving robustness and versatility in complex imaging scenarios such as thick tissue sections and low signal-to-noise ratios. It is applicable to high spatial resolution, high-throughput spatial transcriptome research.