150 results about "H&E Staining Method" patented technology

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

A deep learning-based digital / virtual staining method and system enables the creation of digitally / virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally / virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital / virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally / virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention provides an automatic tumor budding identification method fusing pathological staining migration and YOLO target detection. The method comprises the following steps: training Hamp by using a CUT model; a staining migration model is established from the E image to the immunohistochemical image, and the staining migration model is utilized to carry out Hamp; e, converting the dyed full-width pathological section into a cytokeratin dyed full-width pathological section; and performing tumor budding detection on the generated full-width pathological section dyed by the cytokeratin by using a TB-YOLO model improved on the basis of YOLOv10. According to the method, the CUT is combined for pathological image staining migration, the IHC image generated through TB-YOLO analysis is used for tumor budding detection, meanwhile, accurate translation and detection of positive signals are ensured, richer diagnosis information is provided for pathologists, the diagnosis accuracy is improved, the pathological examination cost of patients is reduced, and the purpose that only Hamp is used for diagnosis is achieved. And E, the tumor budding is accurately detected by the dyed image, so that the comparability of medical images in different dyeing modes and the applicability of a cross-dyeing method are improved.

The invention discloses a UNET supervised network slice HE virtual staining method and system for pathological analysis, and belongs to the technical field of artificial intelligence digital pathological analysis. Global scanning imaging is performed on a pathological slice to obtain an HE scanning image and a fluorescence confocal scanning image, and a UNET supervised network data set is established; a UNET supervised virtual dyeing network model is constructed; training and verifying the UNET supervised virtual dyeing network model by using the UNET supervised network data set; and carrying out HE virtual staining on the fluorescence imaging pathological section image by using the trained UNET supervised virtual staining network model. The method has the remarkable advantages in the aspects of dyeing precision, dyeing efficiency and robustness, and automatic and rapid virtual dyeing of different types of biological tissue sections can be achieved without accurate image registration.

The invention relates to the field of textile printing and dyeing, and discloses a dyeing method and device based on a dyeing prediction model, computer equipment and a storage medium. Processing the dyeing demand through the dyeing prediction model to obtain an initial dyeing process and the credibility of the initial dyeing process; setting experimental parameters of a dyeing test experiment according to the initial dyeing process and the credibility; obtaining a test result of the dyeing test experiment; and determining a target dyeing process meeting the dyeing requirement according to the test result. The problems of less dyeing data and low model result accuracy can be solved.

The invention discloses a virtual staining method and system for a pathological section image, and belongs to the technical field of image processing, and the method comprises the steps: constructing a registration data pair for different staining styles through real staining as training data, meanwhile, a virtual dyeing lookup table model comprising a self-adaptive feature encoder, a self-adaptive weight predictor and a basic virtual dyeing lookup table is constructed, and in the model, a feature map and a weight are generated in a self-adaptive mode based on an input original image; the basic virtual dyeing lookup tables are weighted based on the weights to construct the virtual dyeing lookup table for adaptive fusion of the original image, and then the virtual dyeing lookup table is utilized to perform virtual color dyeing of various chemical dyeing styles, so that the dyeing efficiency in the process is high, the dyeing styles can be diversified, and the image quality is improved. The positions and forms of the pixels are not changed, only color values are changed, dyeing is achieved, the dyeing self-adaption is high, the dyeing quality is guaranteed, and the dyeing area has interpretability.

A deep learning-based digital / virtual staining method and system enables the creation of digitally / virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally / virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital / virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally / virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

The embodiment of the invention provides an image virtual dyeing method and device based on deep learning and a related assembly. The image virtual dyeing method comprises the following steps: utilizing a preprocessed photoacoustic microscopic image and a Hamp; e, performing iterative training on a pre-constructed virtual dyeing model by the dyeing image to obtain a trained virtual dyeing model, and performing virtual dyeing on the new photoacoustic microscopic image by using the trained virtual dyeing model to obtain Hamp; e, dyeing the image of the style (i.e., pseudo Hamp; e, dyeing the image). According to the embodiment of the invention, the image segmentation module is embedded in the generator to help the generator to distinguish cytoplasm and cell nucleus, so that the virtual staining model is constrained, and the generator can better generate a Hamp conforming result; e, dyeing the morphological characteristics of the image to ensure the generated pseudo Hamp; the E dyeing image retains more useful cell nucleus information, and the pre-constructed virtual dyeing model reduces noise in the learning process and effectively prevents the problem of overfitting due to the fact that an image segmentation module is embedded in a generator.

This invention provides a PAM staining method that enables staining suitable for magnified observation of various samples using a range of microscopes, from optical microscopes to electron microscopes. [Solution] A PAM staining method comprising the steps of: silvering a biological sample; coloring the biological sample; and fixing the color of the biological sample, wherein the biological sample is a biologically derived sample that has been oxidized; the silvering step involves immersing the biological sample in a methenamine silver aqueous solution at room temperature, raising the methenamine silver aqueous solution from room temperature to 62°C or higher and 68°C or lower, and carrying out the silvering reaction for 30 to 40 minutes from the start of the reaction at room temperature; the coloring step involves immersing the silvered biological sample in a 0.2% gold chloride aqueous solution at room temperature for 8 to 12 hours; and the fixing step involves immersing the sample in a hard film fixing solution containing 11% to 13% sodium thiosulfate and aluminum potassium sulfate dodecahydrate for 3 minutes or more at room temperature.

A reagent kit, staining method, and application for combining dual immunohistochemistry with elastin fiber staining. The reagent kit includes a combination of primary antibodies, consisting of anti-CK7 and anti-CD34 monoclonal antibodies from different species, such as rabbit anti-human CK7 monoclonal antibody and mouse anti-human CD34 monoclonal antibody. By selecting CK7 and CD34 as the primary antibody combination, the growth pattern of tumor epithelium can be accurately indicated, and the proliferation and thickening of alveolar septal fibrous tissue caused by tumors can be clearly revealed, as opposed to structural changes of alveoli induced by other factors. Combined with independent Victoria blue staining, the elastin fibers of the walls of arteries and veins, as well as the pleura of the lungs, can be clearly visualized, aiding in the assessment of the integrity of elastin fibers on the lung surface and in determining the extent of tumor infiltration.

The invention discloses a full-automatic dyeing machine water-soluble eosin dyeing method based on mechanical arm dynamic control and process optimization, which comprises the following steps: (1) mechanical arm operation logic setting: in the steps of eosin dyeing, washing and 95% ethanol differentiation, the mechanical arm is forbidden to switch idle slot positions by locking slot position distribution; (2) time parameter control: setting the eosin dyeing time to be 2-3 minutes, the washing time to be 30-45 seconds, and the 95% ethanol differentiation time to be 20-30 seconds; and (3) error calibration: performing synchronous verification by adopting a manual timer and a dyeing machine program to ensure that the error between the actual execution time of each step and a set value does not exceed + / -5 seconds.

The invention relates to the technical field of medical inspection equipment, in particular to a CTC enrichment dyeing method and equipment. The method comprises the following steps: fixing a plurality of test tubes filled with enriched staining reagents on a movable test tube rack, placing a plurality of micro-fluidic chips on a movable chip assembly bracket according to the distance between the test tubes on the test tube rack, and arranging a heating platform below the chip assembly bracket; moving the test tube rack to a position above the chip assembly bracket, dumping the test tube rack, and simultaneously scattering the reagents in the plurality of test tubes on the plurality of micro-fluidic chips in a one-to-one correspondence manner; horizontally moving the chip assembly bracket, and blocking the liquid absorption assembly below the micro-fluidic chip by utilizing the edge of the heating platform, so that the liquid absorption assembly falls off from the micro-fluidic chip; and the micro-fluidic chip separated from the liquid absorption assembly is placed on a heating platform to be dried. The method is high in cell enrichment and dyeing operation efficiency, is especially suitable for cell enrichment and dyeing operation of batch CTC chips, and improves the CTC detection efficiency.

The invention belongs to the field of biochemical detection equipment, and discloses a flaking and dyeing all-in-one machine and a flaking and dyeing method.The flaking and dyeing all-in-one machine comprises a machine base, and a workbench, a moving mechanism and a manipulator assembly are arranged on the machine base; the working table is provided with a working area, and the working area is at least provided with a pretreatment working position and a flaking and dyeing working position; the manipulator assembly is movably arranged on the moving mechanism, and the manipulator assembly is located above the working area; the manipulator assembly comprises manipulators, the manipulators at least comprise a first manipulator and a second manipulator, the first manipulator at least has a first moving position and a second moving position, and the second manipulator at least has a third moving position and a fourth moving position; and the first moving position, the second moving position, the third moving position and the fourth moving position are at least matched with the pre-treatment working position and the flaking and dyeing treatment working position. According to the slide preparation and dyeing all-in-one machine, slide preparation and dyeing of liquid-based sample cells can be integrated into all-in-one equipment, and all steps can be independently and circularly carried out.

The invention discloses a pathological image virtual dyeing method and system based on a diffusion model, and belongs to the technical field of cross of pathological image processing, deep learning and computer vision. Through an end-to-end process of data preprocessing, model construction, model training and virtual dyeing reasoning, AF to Hamp is realized; performing non-paired virtual dyeing on the E; the method comprises the following steps: acquiring a non-paired AF pathological image and a Hamp through data preprocessing; e, acquiring a pathological image; generating a trunk through single-step diffusion, and constructing an STAIN model through a multi-modal semantic enhancement module, a local significance perception module and a composite loss function; the STAIN model is trained; and inputting a to-be-processed AF pathological image into the trained STAIN model for virtual dyeing reasoning. According to the method, the multi-step iteration bottleneck of a diffusion model is broken through, and single-step rapid reasoning is realized; cell nucleus and gland pathological structures are accurately reserved; it is guaranteed that the dyeing style conforms to Hamp; e, clinical standard; a lightweight adaptation strategy is adopted; background noise interference is suppressed; the method adapts to non-pairwise data scenes, and the practicability and generalization are improved.

Methods and systems directed to benchtop instruments for sample deposition and staining with machine-learning based image analysis are described. An example system includes an apparatus with a slide processing module, positioned at an upper front position of the apparatus, which has a user interface configured to receive an input from a user that configures a deposition operation and a staining operation for the cellular sample. The system further includes a clamping module positioned at a lower front of the apparatus, an auxiliary systems module positioned at a rear of the apparatus, and an optical imaging and analysis system configured to analyze the cellular sample either on-site or remotely. An example method for depositing, staining and analyzing a cellular sample uses the above-described apparatus.

To provide a slide glass capable of performing staining without peeling off a specimen when performing the staining by using an automatic staining device.SOLUTION: According to the present invention, there is provided a slide glass including a test region including an outer region and a smear region, in which the outer region is formed by performing an antifouling process on the slide glass, the smear region is shaped by being surrounded by the outer region and includes a specimen dropping portion and a staining liquid dropping portion, and the specimen dropping portion and the staining liquid dropping portion are arranged at different positions in the smear region.SELECTED DRAWING: None

The invention discloses a non-degreasing biological tissue dyeing method and device. The dyeing method of the non-degreasing biological tissue comprises the following steps: a sample fixing step: fixing a biological tissue sample by adopting paraformaldehyde and / or glutaraldehyde; and a dyeing step: carrying out electrophoresis dyeing on the fixed biological tissue sample which is not degreased by using a nucleic acid dye and a beta-amyloid protein small molecule dye. According to the non-degreasing biological tissue dyeing method disclosed by the invention, the non-degreasing biological tissue is directly dyed, so that the damage of degreasing operation to a sample is avoided; moreover, the nucleic acid dye is combined with the beta-amyloid protein small molecule dye, so that the whole brain sample can be quickly and efficiently dyed, and high-resolution fluorescence labeling and three-dimensional space distribution imaging of beta-amyloid protein plaques in the whole brain range can be realized; the method is of great significance in revealing the spatial distribution pattern of beta-amyloid protein and explaining the pathogenesis of Alzheimer's disease.

This invention discloses a virtual immunohistochemical staining method and system based on relation generative adversarial networks (GANs) and sequence slide data, comprising the following steps: Step S1, constructing a sequence slide dataset, wherein each H&E-stained slide is followed by three consecutive Ki67-stained slides; Step S2, constructing a relation generative adversarial network (GAN) model, the model comprising two ResNet generators and two relation discriminators, used to train and evaluate the credibility of the sequence relationship between image pairs; Step S3, performing two-stage training on the relation generative adversarial network model, namely, basic style pre-training and Z-axis alignment relation adaptation. This invention fundamentally solves the Z-axis alignment problem, resulting in higher image fidelity and pathological accuracy, significantly improving clinical application value, and providing new research resources and evaluation paradigms.

The centrifugal immunohistochemical staining method comprises the following steps that a slide is placed in a slide bin located on a centrifugal staining disc, a supporting part is arranged in the slide bin so that a gap can be formed between the bottom side of the slide and the bottom of the bin, and a section to be stained is attached to the bottom side of the slide; rotating the centrifugal dyeing disc, starting a dripping dyeing assembly, dripping reaction liquid towards one side of the slide by utilizing a dripping dyeing head at the tail end of the dripping dyeing assembly, and enabling the reaction liquid to flow to the section area; after dripping dyeing is completed, the centrifugal dyeing disc rotates at a high speed for a certain time, and the slide is directly spin-dried under the centrifugal effect. According to the invention, after the section is subjected to drop dyeing by the reaction liquid, the high-speed centrifugal principle is directly utilized, the centrifugal dyeing disc drives the surrounding slide to do centrifugal motion, so that the slide can be spin-dried, the steps of washing and drying by wind are not needed, and the method is essentially different from the existing method, so that the whole dyeing process steps are reduced, functional parts such as washing and blowing are omitted, and the cost is reduced. The dyeing machine is simpler and more compact in structure, and the dyeing efficiency is remarkably improved.

The invention provides a PN junction dyeing method and a PN junction characterization method.The PN junction dyeing method comprises the steps that a to-be-dyed sample with a to-be-dyed PN junction exposed out of the cross section is provided; preparing a first solution; a second solution is added into the first solution to form a first staining solution, the first staining solution comprises a first component and a second component, and the second component is the same as the second solution; a first substance is added into the first staining solution to form a second staining solution, the second component and the first substance react at the reaction rate smaller than a preset threshold value to generate hydrofluoric acid, and the first substance and the second component react completely after a first duration; and within a second duration of adding the first substance into the first staining solution, immersing the cross section of the PN junction to be dyed exposed in the sample to be dyed into a second staining solution, and dyeing the PN junction to be dyed, the second duration being shorter than the first duration. According to the PN junction dyeing method, the controllability and safety in the operation process of the PN junction dyeing method can be improved.

The invention relates to a method for staining bacteria, comprising the steps of a) staining a bacteria sample by means of a solution of crystal violet, b) adding Lugol's solution to the stained bacteria sample, c) rinsing with distilled water, d) adding a solution of safranine-O and fuchsin in diethylene glycol monoethyl ether, and e) rinsing with distilled water, wherein the method is carried out substantially without alkanol.

To provide a staining device capable of speeding up the staining of a biological tissue.SOLUTION: The staining apparatus 1 includes a cathode chamber 2, an anode chamber 4, a stain solution 21 containing an ionic dye 23 and contained in one of the cathode chamber 2 and the anode chamber 4, a buffer solution 22 contained in the other of the cathode chamber 2 and the anode chamber 4, a first electrode 11 immersed in the stain solution 21, a second electrode 12 immersed in the buffer solution 22, and a communicating tube 3 connecting the cathode chamber 2 and the anode chamber 4. When the living tissue 20 is arranged so as to block the cross section of the communicating tube 3 and electricity is made to flow between the first electrode 11 and the second electrode 12, the dye 23 is electrophoresed so as to pass through the living tissue 20.SELECTED DRAWING: Figure 1

This invention discloses a high-throughput automated immunofluorescence staining device and its staining method. The device includes a liquid injection motion module, a reaction chamber module, a supply and waste collection module, and a control system. The liquid injection motion module includes an X-axis beam and a pipetting and auxiliary material robotic arm, which are driven in parallel to achieve sequential operation. The reaction chamber module includes a temperature-controlled station, a low-temperature reagent storage unit, and an auxiliary material supply unit. The supply and waste collection module includes a waste removal and cleaning unit and an auxiliary material waste collection unit. This invention achieves a 96-station array through a temperature-controlled reaction station, combined with the dual-drive parallel operation of the pipetting and auxiliary material robotic arms, eliminating action waiting time and significantly improving throughput. The use of a closed semiconductor cooling component linked with an automatic flip-top mechanism ensures uniform reagent activity over a long period. A plate-mounted small vacuum pump drives a rigid pipeline with deep-hole waste suction, combined with high-flow-rate wide-area spraying, to thoroughly remove waste liquid and reduce cross-contamination. This achieves full-process automation, significantly improving staining quality and efficiency.

The invention discloses a dyeing method and kit for an osteocyte indentation-tubule system, and the dyeing method comprises the following steps: (1) preparing (0.5-1) mol / L AgNO3 as a solution I, (2) preparing 0.05%-2% (w / v) of B-type gelatin and 0.05%-5% (v / v) of formic acid as a solution II; and (3) mixing the solution I and the solution II in a volume ratio of (4-1): 1, and dyeing. The optimized dyeing step shows clear and reliable dyeing mode and strength in different species and disease models, and compared with a traditional method, the method has the advantages that more bone cell indentation-tubule systems can be dyed, and the density of the bone cell indentation-tubule systems can be increased.

Staining devices and methods for automated systems are provided herein. A dyeing method includes disposing a substrate proximate a dyed surface to establish an overlap between the dyed surface and the substrate. The staining method further includes maintaining a gap between the stained surface and the substrate to define a chromosome product according to the overlap and the gap, where the chromosome product has one or more meniscus edges. The staining method further includes disposing a portion of the staining fluid over the staining surface and distributing the staining fluid over the substrate.

The invention discloses a data staining method, system and device and a storage medium, and relates to the technical field of data security, the method comprises the following steps: extracting statistical features of training data, and obtaining predefined staining features; using a feature generator to fuse the statistical features and predefined dyeing features to obtain fused features, and training a dyeing model based on the fused features so as to embed the predefined dyeing features into dyeing data generated by the dyeing model; and detecting whether the dyeing data carries the predefined dyeing characteristics or not through a traceability model so as to determine the source of the dyeing data. By training the dyeing model, the generated dyeing data keeps the characteristics of the original data, and the invisible statistical beacons are embedded, so that the security and traceability of the data are ensured. By detecting whether the dyeing data carries the predefined dyeing features, the source of the data can be accurately identified, and the authenticity and integrity of the data are ensured.

The invention belongs to the technical field of pathological tissue section staining, and particularly relates to a hematoxylin-eosin staining method based on short-time immersion and chaotropic standing diffusion and application of the hematoxylin-eosin staining method. According to the hematoxylin-eosin staining method based on short-time immersion and chaotropic standing diffusion, the staining process time can be remarkably shortened, the working efficiency of pathological section staining can be greatly improved, the staining solution consumption and the cross contamination risk can be effectively reduced, the experiment cost is saved, and the reliability of the staining result is guaranteed; meanwhile, the dyeing uniformity and repeatability can be remarkably improved, the result deviation caused by operation difference is reduced, the dependence on the experience of an operator is reduced, and standardized popularization and application are facilitated; besides, the dyeing method disclosed by the invention is wide in applicability, can meet the dyeing requirements of conventional paraffin sections, frozen sections and trace tissue samples, can meet the use requirements under different pathological detection scenes, and has outstanding clinical and scientific research application prospects.

The present invention addresses the problem of providing a method for staining a cell membrane protein with high accuracy. Provided is a method for detecting a cell membrane protein in an embedded immobilized tissue, the method comprising an embedding medium removal step for removing an embedding medium by bringing the immobilized tissue, which contains an embedding medium in a solid state, into contact with a solution for removal without baking.

The present invention discloses an intelligent virtual staining method and device for Epstein-Barr virus-related tumor pathology slice images, and relates to the field of image intelligent processing technology. The method comprises: obtaining H&E images of in vitro tissue slices of Epstein-Barr virus-positive cells, performing EBER in situ hybridization on the slices after elution and staining, and then scanning them into slice images, performing image registration on the above two images, and obtaining training images after marking Epstein-Barr virus-positive cells; then obtaining images of in vitro tissue slices of Epstein-Barr virus-negative cells as comparison images, and training a virtual staining model that can intelligently distinguish and virtually stain Epstein-Barr virus-positive cells in H&E slice images based on the training images and the comparison images. The present invention can reduce the complexity of the operation process of artificial intelligence processing slice image technology, improve the intelligence level of artificial intelligence processing slice image technology, and replace the biological marker method with intelligent virtual staining, which has great scientific significance, social value and economic benefits.