109 results about "Cell segmentation" patented technology

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 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 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 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 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 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 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.

The invention discloses an intelligent cerebral apoplexy cell state recognition method based on Cellpose and fluorescence image analysis and application of the intelligent cerebral apoplexy cell state recognition method in drug effect screening. The intelligent cerebral apoplexy cell state recognition method comprises the following steps: S1, establishing a cerebral apoplexy ischemia reperfusion cell model; s2, adding a nitrite fluorescent probe into the cells subjected to ischemia reperfusion treatment to obtain a fluorescent microscopic image of the cells; s3, importing the fluorescence microscopic image into a Cellpose model for cell segmentation, and obtaining an ROI region of each cell; s4, extracting fluorescence intensity characteristics and cell morphological characteristics in the ROI region of each cell; and S5, inputting the extracted multi-dimensional features into a classification model for cell state recognition, so as to distinguish a single cell into three states of health, mild injury or serious injury. According to the method, a complete link of image acquisition-Cellpose analysis-feature extraction-state discrimination-drug effect evaluation is constructed, and the practical application value of a deep learning image algorithm in disease model research and drug screening scenes is expanded.

The invention belongs to the technical field of document image processing and data mining, and particularly discloses a geoscience field literature scatter diagram data extraction method, a storage medium and equipment, and the method comprises the steps: classifying pictures extracted from geoscience field literature PDF into conventional scatter diagram pictures and table type scatter diagram pictures; positioning an independent scatter diagram region in the conventional scatter diagram picture by adopting a target detection model, segmenting the independent scatter diagram region to obtain a first type of independent scatter diagrams, extracting cells in the table type scatter diagram picture by adopting a cell segmentation method based on morphological operation and line segment intersection detection, and obtaining a second type of independent scatter diagrams; the cells are spliced with the coordinate axis area of the picture to obtain a second type of independent scatter diagrams; and inputting the first type of independent scatter diagrams and the second type of independent scatter diagrams into a scatter diagram data extraction model to obtain coordinate axis scale lines, then converting pixel coordinates of scatter points into actual data values, and outputting scatter diagram data. According to the method, the scatter diagram data can be automatically and accurately extracted from the geoscience literature.

A deep neural network-based method for detecting living cell morphology may include identifying and locating one or more living cells within an acquired image to be detected by using a deep neural network-based target detection model, so as to extract one or more living single cell images. segmenting the image of the one or more living single cells by using a deep neural network-based cell segmentation model, so as to obtain one or more feature part of the one or more living single cells. and analyzing and determining a morphological parameter of the one or more living single cells based on the one or more feature parts. Thus, the activity of the detected cells can be ensured, and a non-destructive, accurate, and rapid detection of living cell morphology can be achieved.

The present application relates to the technical field of cell image analysis and medical artificial intelligence, and discloses a cell segmentation and adaptive cascade reasoning method and system based on prior frame guidance. The method comprises: acquiring a multi-modal cell image with a detection frame; performing adaptive histogram equalization preprocessing on the image and executing global preliminary screening segmentation; calculating a multi-dimensional weighted score of the mask and the detection frame, if the score is lower than a first matching threshold, extracting a local image block to dynamically adjust a flow field threshold and an estimated diameter for cascade retry; if the score after retrying is lower than a second threshold, triggering a label consistency protection mechanism to discard or back up poor samples; finally, performing connected domain purification on the retained mask, and calculating a topological solidity, and performing convex hull reconstruction on the mask with low solidity. Through two-stage reasoning, double-track quality filtering and topological constraint, the present application effectively improves the segmentation effect of dense and irregular cells and improves the generalization performance of downstream analysis.

The present invention relates to a cell analysis system based on virtual fluorescence generation technology, and a method therefor. According to the present invention, the system comprises: an input unit that receives a quantitative phase image of a sample including at least one cell through digital holography microscopy; and a cell identification unit that identifies at least one living cell by applying, to the received quantitative phase image, a cell segmentation mask generation model and a nuclei segmentation mask generation model constructed previously, and that masks a position of the nucleus of the living cell to derive an identification result. The system may further comprise a post-processing unit that derives a final result by applying a preset post-processing method to the derived identification result.

The application discloses a kind of cell autophagy immunofluorescence image analysis system and method based on deep learning, including cell segmentation module, GFP-LC3 positive bright spot segmentation module, quantitative analysis module.Cell segmentation module carries out segmentation to the cell region in cell autophagy immunofluorescence image;GFP-LC3 positive bright spot segmentation module carries out segmentation to GFP-LC3 positive bright spot in cell autophagy immunofluorescence image;Quantitative analysis module carries out the statistics and calculation of quantity, area and fluorescence intensity to cell region and GFP-LC3 positive bright spot region in cell autophagy immunofluorescence image respectively, and judges whether cell occurs autophagy.The application is simple to operate, and has accurate deep learning algorithm and quantitative analysis function.

To provide a system and a method for automated, unsupervised, parameter-free segmentation of a single cell and other object in an image generated by a fluorescence microscope.SOLUTION: A method for improving both initial image quality and automatic segmentation on an image is typically performed on a digital image by a computer executing an appropriate software stored in a memory or by a processor.SELECTED DRAWING: Figure 1

The invention aims to provide a biological prior-based cell medical image segmentation method, which comprises the following steps of: A, constructing a neural network which comprises a coding network and a decoding network; the coding network comprises a VSS branch, a YOLO network and a GNN branch; b, inputting the original image into a decoding network, dividing the original image into two paths, and obtaining a first preprocessing result after the first path is processed by a convolutional layer and an LN function in sequence; the first preprocessing result is input into a VSS branch to be processed, and the result is input into a decoding network; in the second path, each single cell in the image is detected and obtained through YOLO network processing, the background is removed, and the background-removed image is processed through three convolutional layers in sequence to obtain a second preprocessing result; the second preprocessing result is input into a GNN branch to be processed, and the result is input into a decoding network; and C, the decoding network decodes each input result to obtain a final segmentation processing result. According to the method, the cell segmentation precision can be improved, and powerful support is provided for histological analysis and diagnosis.

The invention relates to a multi-target combined toxicity screening method and system based on deep learning, and belongs to the technical field of food safety and biology. The method comprises the following steps: carrying out combined co-incubation on HepG2 cells and to-be-detected samples with different concentration proportions, and labeling a subcellular structure by using a fluorescent probe; acquiring a multi-channel fluorescence image; performing cell segmentation on the image; extracting phenotypic characteristics of the cells; and based on a ResNet18 model, carrying out regression training on the extracted phenotypic features and the collaborative toxicity score calculated by the HSA model, and establishing a toxicity prediction model. Compared with a traditional combined toxicity screening method, on the premise that it is ensured that the combined toxicity effect prediction accuracy is close to that of a traditional experimental method, the multi-dimensional phenotypic characteristics of the cells are extracted through multi-organ dyeing, so that the combined toxicity effect is comprehensively analyzed, and a promising tool is provided for achieving combined toxicity high-throughput screening.

This invention discloses a cell segmentation method, device, storage medium, and program product based on spatial omics sequencing. The method includes: acquiring a whole-slice image, wherein the whole-slice image indicates the distribution characteristics of spatial omics signals in the tissue slice; dividing the whole-slice image into multiple image blocks, identifying whether heterogeneous regions exist in the multiple image blocks, and obtaining identification results; preprocessing each image block according to the identification results to obtain preprocessed image data corresponding to each image block; forming the input of a deep learning segmentation model based on the preprocessed image data corresponding to each image block, and outputting the segmentation results of each image block through the deep learning segmentation model; and obtaining each cell region in the whole-slice image based on the segmentation results of each image block.

This paper discloses a Transformer-based end-to-end cell segmentation and tracking method and system. This method employs an improved model based on the Mask DINO model to implement cell segmentation and tracking. The method uses a top-K query selection strategy to select the K feature points with the highest probability in the output feature map as object queries, and uses the trajectory from the previous frame t-1 as the tracking query in the current frame t; additionally generates a splitting query representing mitosis; uses a Transformer decoder to decode the object query, tracking query, and splitting query; and uses a classification head to predict the classification labels, rotated bounding boxes, and segmentation masks of the cells in image I of the current frame t. This method aims to address challenges in cell segmentation and tracking scenarios and improve the accuracy of cell segmentation and tracking.