33 results about "Stain tissue" patented technology

Alignment of a 2D image to a corresponding 3D image is provided by writing a pattern into a 3D sample. The pattern is at known positions in the 3D image, and provides visible reference features in the 2D image. This permits accurate determination of the plane in the 3D image that corresponds to the 2D image. Improvements in this work relate to : sample preparation, for example direct staining of the tissue with a dye that binds to the tissue; alignment accuracy, for example using patterns and optics that provide better depth information; and use of the 2D images to render a 3D image where the reference features from the pattern enable the 3D rendering.

The present application relates to a kind of biological tissue staining machines, including main component, moving mechanism and liquid suction dehumidification structure, main component includes shell, work tank, installation groove, mounting bracket and bearing frame, the top of shell is equipped with work tank, the bottom of work tank is equipped with installation groove. By setting up liquid suction dehumidification structure, can be actively connected the dyeing liquid, dehydrating agent dropped in dyeing process, avoid the formation of effusion in the bottom of work tank, reduce reagent waste from the source;Prevent the abnormal humidity in tank caused by effusion volatilization simultaneously, guarantee the reagent concentration stability of subsequent dyeing step, and avoid effusion corrosion equipment components, pollute the tissue to be dyed, significantly improve the accuracy of dyeing result.

This invention discloses a visual analysis and prediction method for the ZNF469 gene mutation status in colorectal cancer, belonging to the field of intelligent medical image analysis technology. It involves acquiring whole-slice images of H&E-stained tissue from colorectal cancer patients and corresponding ZNF469 gene mutation status data, constructing an image label pairing dataset; preprocessing the whole-slice images, cutting them into image patches, and using a pre-trained three-class classification model to filter out cancerous region image patches; extracting the macroscopic structural features of each image patch, and simultaneously constructing a nuclear perception map (Transformer) to extract microscopic nuclear morphological features and cell nuclear spatial topological features. This invention achieves rapid prediction of the ZNF469 gene mutation status in colorectal cancer based on pathological images, without relying on gene sequencing technology, reducing detection costs, shortening the diagnostic cycle, and enabling simultaneous mutation status prediction during pathological slide reading, thus improving the efficiency of colorectal cancer diagnosis.

A computer-implemented method for processing medical images, the method including receiving one or more of medical images of at least one pathology specimen, the pathology specimen being associated with a patient, wherein the medical image is a stained histology image. The method may further include receiving a stain type associated with the one or more medical images and identifying a color vision deficiency for one or more users. Next the method may include identifying a pixel transformation for the one or more medical images based on the stain type and color vision deficiency of the one or more users. Next the method may include applying a pixel transformation to each pixel within the one or more medical images. Lastly the method may include displaying the transformed one or more medical images to the one or more users.

This provides a system and method for analyzing and diagnosing conditions, such as cancer, based upon stained tissue sample slides prepared by users from patient tissue taken, for example, in a biopsy procedure. A convolutional neural network (CNN) is generated at training time. Slide images are acquired / scanned, and individual cell nuclei from the images are annotated, using IHC. H&E images are acquired / scanned prior to a washout intermediate step from the same slides / tissue samples. IHC is performed on the same tissue layer, which was scanned and used to retrospectively annotate the H&E WSI. IHC is used to annotate a ground truth mask for machine learning. The resultant process achieves a high degree of spatial resolution in detecting individual IHC positive nuclei, yielding process that is almost entirely automated, employing materials that are commonly available in clinical practice. In runtime, users access the CNN to perform analysis on patient slides.

The invention discloses an immunotherapy curative effect prediction method based on pathological section deep learning scoring, and the method comprises the steps: pathological image acquisition, image segmentation, image quality evaluation and screening, image color normalization, feature extraction and model training, and patient-level score generation and curative effect prediction index establishment, using a digital slice scanner to scan the Hamp of the small cell lung cancer patient; e, digitizing the dyed tissue sections to obtain high-resolution WSI images, and segmenting the images, namely segmenting each WSI image into a plurality of image blocks with fixed sizes to form a standardized tile image set. According to the immunotherapy curative effect prediction method based on pathological section deep learning scoring, through data standardization processing, image segmentation, quality screening and color normalization, staining difference and invalid data interference are reduced, input data quality is improved, and through efficient feature extraction, deep features of pathological images are automatically learned by using a Resnet50 model.

The present invention belongs to the technical field of dyeing instruments and discloses a miniature dyeing device, comprising a protective housing, a control module for controlling the operation of the device disposed within the protective housing; a pull-out plate that can be pulled out of the protective housing, a glass slide for carrying tissue to be stained positioned within the pull-out plate; a positioning platform rotatably disposed on the protective housing, a rotating head disposed on the protective housing that is lifted and rotated in correspondence with the positioning platform; a consumables box positioned on the positioning platform, the rotating head being transmission-connected to the consumables box, a plurality of containers for dyeing consumables disposed within the consumables box; a consumables pressing head corresponding to the tissue on the glass slide, for pressing the container to quantitatively supply the dyeing consumables to the tissue. The present invention has a compact structure, a small device size, is easy to carry, and is suitable for different use environments. At the same time, it can accurately control the output of reagents, improve the accuracy of dyeing, reduce waste, is convenient and fast, and is simple to operate, making it suitable for laboratories, hospitals, industries, and other fields.

Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a sample section. In particular, aspects of the present disclosure relate to: accessing a multiplex immunofluorescence image of a sample section comprising a first channel for a nuclear marker and a second channel for an epithelial tumor marker, wherein the sample section comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine learning model; receiving an output of the machine learning model, the output corresponding to a prediction that the multiplex immunofluorescence image comprises one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine learning model; and outputting the mask for the subsequent image processing.

A method for training a machine learning model and a computing system performing same, wherein the machine learning model is trained to analyze biological tissue slide images stained by a immunohistochemical staining method for staining tissues expressing specific biomarkers and thus can be used to further elaborately analyze expression levels of biomarkers, etc., whereby a determination can be made on pathological specimen images by analyzing expression levels of biomarkers, etc. A method and system for training a machine learning model using training data, corresponding to immunohistochemically stained images, generated from multiple feature vectors calculated based on various staining intensity criteria.

A computer-implemented method for processing an image, comprising: receiving at least one image of an unsectioned sample; and processing at least the image using a neural network arrangement configured to perform digital staining to obtain a digitally stained histological image of the unsectioned sample. The image of the unsectioned sample is obtained by an imaging system having an excitation light source that provides light to cause the emission of light from one or more substances, such as one or more photoluminescent substances, in the unsectioned sample to image the unsectioned sample.The digitally stained histological image generally corresponds to, or is substantially equivalent to, an image of the non-sectioned specimen that has been chemically stained using one or more chemical stains. The non-sectioned specimen may be an unstained biological specimen with endogenous fluorophores, and the image may be an autofluorescence image of the unstained biological specimen.

An apparatus and methods for determining features in an image of a Hematoxylin and Eosin (H&E) stained tissue sample. An apparatus can be configured to apply a machine learning model to the H&E stained tissue sample image to determine features in the image. Applying the machine learning model can include performing convolution operations on the H&E stained tissue sample image to generate a plurality of initial feature maps, applying octave-convolution-first-layer operations on the initial feature maps to generate initial high-frequency feature maps and low-frequency feature maps, applying octave-convolution operations on the high-frequency initial feature maps to generate refined high-frequency feature maps, applying octave-convolution operations on the low-frequency initial feature maps to generate refined low-frequency feature maps, and applying octave-convolution last layer operations on the refined high-frequency feature maps and the refined low-frequency feature maps to produce combined feature maps.

1. The name of the product under this design: automatic dyeing machine. 2. Purpose of this product design: Used for staining tissues and cells before pathological analysis. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture. 5. Other situations that require explanation: ABCD are made of transparent materials.

Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a slice of specimen. Particularly, aspects of the present disclosure are directed to accessing a multiplex immunofluorescence image of a slice of specimen comprising a first channel for a nuclei marker and a second channel for an epithelial tumor marker, wherein the slice of specimen comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine-learning model; receiving an output of the machine-learning model corresponding to a prediction that the multiplex immunofluorescence image includes one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine-learning model; and outputting the mask for the subsequent image processing.

Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides or virtual tissue sections generated using artificial neural networks (ANNs). In some implementations, the created digital slides or a virtual tissue sections can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.

The invention relates to a large-size pathological section panorama generation method based on repetition degree positioning. The method comprises the following steps: firstly, acquiring a dyed tissue slice image, and preprocessing the dyed tissue slice image; then, carrying out illumination correction, contrast enhancement and denoising processing on the preprocessed dyed tissue slice image; then, determining an overlapping region sub-image based on the dyed tissue slice image, and determining a feature point transformation relation based on the overlapping region sub-image; and finally, carrying out in-line splicing and inter-line splicing on the dyed tissue section image based on the feature point transformation relation to obtain a pathological section panorama. That is to say, through the preprocessing processes of image quality compression, edge detection and the like, blank images are effectively removed, and illumination correction, contrast enhancement and denoising processing are performed, so that the brightness of the generated panorama is uniform, accurate positioning is performed based on repetition, and the splicing accuracy and efficiency are improved.

An autonomous quality and hallucination assessment method (AQuA) is disclosed, designed for either virtual tissue staining or histochemical staining. AQuA uses a trained neural network to autonomously achieve 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to ground truth, also presenting an agreement of 98.5% with the manual assessments made by a group of board-certified pathologists. AQuA achieves super-human performance in identifying realistic-looking, virtually stained hallucinatory images that would normally mislead human diagnosticians. The wide adaptability of AQuA is demonstrated across various virtually and histochemically stained human tissue images and showcase its strong external generalization to detect unseen hallucination patterns of virtual staining network models as well as artifacts observed in the traditional histochemical staining workflow.

1. The name of the design product: reagent jar. 2. The use of the design product: the design product is used for staining tissue slides in the pathology department of a hospital. 3. The design points of the design product: the shape of the product. 4. The picture or photo that best indicates the design points: perspective view 1.

A method for manufacturing a histologic tissue sample support device includes molding a tissue cassette, molding a frame, and coupling the tissue cassette to the frame by assembling a frame-cassette connector. The tissue cassette has a recess including at least one side wall and a bottom wall. The tissue cassette is formed of a first material that can be successfully sectioned in a microtome and is resistant to degradation from solvents and chemicals used to fix, process and stain tissue. The frame includes a bottom edge and is formed of a second material different from the first material and more rigid than the first material. The frame-cassette connector includes a first retaining structure formed integrally with the frame extending through a second retaining structure formed integrally with the cassette. The frame is capable of being decoupled from the cassette by separating the frame-cassette connector.

The invention discloses a tumor marker detection method and system based on DNA quantitative analysis in the technical field of cell images. The detection method comprises the following steps: S1, staining a tissue / cell section; s2, scanning the stained tissue / cell section to obtain a digital section image; s3, performing cell segmentation on the slice image to obtain a segmented image containing a cell mask region, and performing DNA quantitative analysis and cytological analysis and classification on the segmented image; s4, performing virtual dyeing on the slice image; and S5, acquiring a DNA quantitative analysis result and a cytological analysis and classification result, and jointly outputting the results in combination with the virtual dyeing image. According to the detection method, a complete solution with high objectivity and high clinical practicability is jointly formed through collaborative diagnosis of DNA quantitative analysis and cytological analysis and by combining virtual staining, the collaborative diagnosis efficiency is generated, and the problem that practical application is difficult due to the fact that current pathological AI diagnosis is insufficient in accuracy is solved.

The invention discloses a virtual immunohistochemical staining method, device and equipment for a tissue slice and a medium, and relates to the field of computational pathology counting. E, constructing a cell map from the dyed image to generate corrected IHC semantic features of each node; generating a history Hamp by using an image generator; e, dyeing a historical virtual IHC image corresponding to the image; using the historical real IHC image and the historical virtual IHC image to optimize the parameters of the double-branch discriminator; optimizing image generator parameters by using a discrimination result of the historical virtual IHC image output by the discriminator, the corrected IHC semantic features and semantic features in the historical virtual IHC image to obtain a new image generator network, and performing optimization for multiple times to obtain a target image generator; outputting the Hamp of the tissue section to be dyed by using the generator; and E, dyeing the target virtual IHC image of the image. And performing virtual IHC with clear texture and accurate pathological features on the tissue slices.

Staining compositions and methods to stain a tissue are disclosed herein. The staining composition includes a diluent and a colorant comprising pigment particles having a D50 particle size of about 5 microns or less and a liquid carrier. The composition has a viscosity of about 10 Krebs Units or less. Using the staining composition, tissue may be stained directly at the point of excision from the patient in a minimum amount of time so that the specimen is identifiable and distinguishable at every point of risk from collection to analysis and reporting.

Various examples of the present disclosure relate to techniques for virtually staining a tissue sample. One or more output images having a virtual stain are determined. These techniques are directed to a more robust process. To this end, a plurality of imaging data sets are used as inputs for a digital process, the plurality of imaging data sets having a plurality of different phase contrasts.

The biological tissue dyeing machine comprises a main body assembly, a moving mechanism and a liquid absorbing and dehumidifying structure, the main body assembly comprises a shell, a working groove, a mounting groove, a mounting frame and a bearing frame, the working groove is formed in the top of the shell, and the mounting groove is formed in the bottom of the working groove. By arranging the liquid absorbing and dehumidifying structure, staining liquid and a dehydrating agent dripping in the staining process can be actively received, liquid is prevented from forming accumulated liquid at the bottom of the working tank, and reagent waste is reduced from the source; meanwhile, abnormal humidity in the tank caused by accumulated liquid volatilization is prevented, the stability of the reagent concentration in the subsequent dyeing step is guaranteed, the accumulated liquid is prevented from corroding equipment parts and polluting to-be-dyed tissues, and the accuracy of the dyeing result is remarkably improved.

A histologic tissue sample support device includes a tissue cassette, a frame, and a lid. The tissue cassette has a recess including at least one side wall and a bottom wall and is formed of material that can be successfully sectioned in a microtome and is resistant to degradation from solvents and chemicals used to fix, process and stain tissue. The lid is coupled to the frame and includes a platen configured to be received within the recess of the tissue cassette. The tissue cassette is movably coupled to the frame by a frame-cassette connector. The lid and the tissue cassette are capable of moving from a first position to a second position with respect to the frame, and in the second position the bottom wall and at least a portion of the side wall extend beyond a bottom edge of the frame for sectioning in a microtome.

Alignment of a 2D image to a corresponding 3D image is provided by writing a pattern into a 3D sample. The pattern is at known positions in the 3D image, and provides visible reference features in the 2D image. This permits accurate determination of the plane in the 3D image that corresponds to the 2D image. Improvements in this work relate to: sample preparation, for example direct staining of the tissue with a dye that binds to the tissue; alignment accuracy, for example using patterns and optics that provide better depth information; and use of the 2D images to render a 3D image where the reference features from the pattern enable the 3D rendering.

The invention discloses a micro-fluidic chip, a tissue dyeing device comprising the micro-fluidic chip and a tissue dyeing method. The micro-fluidic chip comprises an upper slide, wherein the upper slide is provided with an inlet hole and an outlet hole; the double-faced adhesive tape layer is used for removing part of double-faced adhesive tape through cutting so as to present an inflow channel, a dyeing cavity and an outflow channel of the corresponding micro-fluidic chip, the inflow channel is communicated with the inlet hole, and the outflow channel is communicated with the outlet hole; a to-be-dyed tissue is arranged on the lower slide; wherein the upper slide and the lower slide are adhered into a whole through the double-sided adhesive layer. A reagent bin is arranged at an inlet of the micro-fluidic chip, the reagent bin is designed to be open, and an outlet of the micro-fluidic chip is connected with a pressure pump. A reagent in the reagent bin is pumped into the dyeing cavity of the micro-fluidic chip through the pressure pump for dyeing, and is discharged after dyeing. According to the invention, the complexity and cost of the system are reduced, and meanwhile, the waste of reagents is reduced.

The utility model belongs to the technical field of pathological staining, and particularly relates to a staining device for pathological technology, which comprises a dewaxing staining machine, the dewaxing staining machine comprises a base, the top of the base is fixedly connected with a clamping seat, an inner cavity of the clamping seat is movably connected with a dye vat, and the back side of the top of the base is fixedly connected with a supporting rod; the motor is started to drive the blades to rotate, at the moment, the third pipeline begins to suck dimethyl benzene volatile gas in the shell, the volatile gas is sucked into the filter bin through the first pipeline, and the gas sucked into the filter bin is firstly subjected to basic filtration through the filter screen and then is subjected to deep filtration through the poison filter box; and finally, the liquid is discharged through the air outlet, so that the filtering effect is achieved, and the problems that in the dyeing process, a xylene solvent needs to be used when the tissue to be dyed is dewaxed, but xylene has volatility and is a toxic substance, and the health may be harmed due to long-term contact or inhalation of steam of the xylene are solved.

This application discloses a virtual staining method, apparatus, device, and medium for images, relating to the field of image processing technology. The method includes: inputting an image of tissue to be stained into a preset morphological instance detection model to locate morphological structural units of a preset category in the image, obtaining corresponding bounding box sets, and generating a target mask; inputting the image of tissue to be stained into a preset virtual staining generation model, performing staining transformation on the image of tissue to be stained through a global virtual staining path to generate a global staining result, and performing staining processing on the image regions where the morphological structural units in the image of tissue to be stained are located through instance virtual staining paths and according to the bounding box sets to generate instance staining results; performing geometric deformation correction processing on the instance staining results and outputting corrected instance staining results; and performing weighted summation and residual repair processing on the global staining results, corrected instance staining results, and target mask to generate a target virtual staining image. This achieves high-quality virtual staining.

The application provides an RNA composition, a primer composition and application thereof in identifying a body fluid stain tissue source. The RNA composition comprises HBA mRNA, HBB mRNA, STC1 mRNA, MMP10 mRNA, MUC22 mRNA, CYP2B7P mRNA, STATH mRNA, HTN3 mRNA, PRM2 mRNA and SEMG1 mRNA, and by detecting the same, five common body fluids (peripheral blood, menstrual blood, vaginal secretion, saliva and semen) in forensic identification can be identified at the same time. The RNA composition provided by the application has the advantages of wide coverage, high accuracy, good specificity, high sensitivity, capability of detecting mixed samples and real samples, and high stability.