47 results about "Haematoxylin" patented technology

Techniques for using combination stain types for machine learning models for digital pathology are described herein. In an example, a system accesses a first image of a sample comprising an immunohistochemistry (IHC) stain for a biomarker. The system accesses a second image of the sample comprising a hematoxylin and eosin (H&E) stain for nuclei. The system can segment tissue regions in the one or more first images and the second image, partitions the tissue regions in the one or more first images and the second image, and extracts features from the set of tiles using a feature extractor. The system can generate, by a machine-learning model, an output classification indicating a first phenotype based on the features extracted from the set of tiles. The machine-learning model can include one or more classifiers and an aggregation model that provides an aggregated output for the set of tiles.

The present disclosure relates to a method for selection of ovarian cancer patients for treatment with PARP inhibitors This involves 1) preparing whole slide images of tissues of ovarian cancer patient to be treated 2) training and validating AI model built on Resnet-50 to recognize morphological features from tiles of hematoxylin and eosin stained whole slide images of tissues featuring the presence of homologous recombination deficiency as annotated by next generation sequencing 3) using the model to extract morphological features from the tiles of hematoxylin and eosin stained whole slide images of tissues of ovarian cancer patients, generating tile level predictions, aggregating tile level predictions to generate a probability score and selecting the patient for treatment with PARP inhibitors 4) administering PARP inhibitors treatment to the patient selected based on probability score reaching a predetermined threshold. Computer systems to implement the method are also disclosed.

Methods for dyeing suture material that include incubating the suture material in a high temperature staining bath comprising hematein are disclosed. The staining bath may include a nonionic surfactant. The method is carried out in the absence of chromium salts. Also disclosed are (i) a dyed suture material produced by the methods and (ii) a surgical suturing kit that includes the dyed suture material.

The invention provides an application of farrerol in preparation of a medicine for preventing osteoporotic diseases caused by inflammatory bowel diseases, and belongs to the technical field of inflammatory bone disease treatment.The application comprises the steps that firstly, an ulcerative colitis (UC) mouse is constructed to simulate occurrence of the osteoporosis induced by enteritis, and the occurrence of the osteoporosis induced by enteritis is simulated through microcomputer tomography (CT), hematoxylin and eosin (Hamp; e) and tartrate-resistant acid phosphatase (TRAP) staining are used for analyzing the bone destruction degree and the number of osteoclasts, and the influence of the farrerol on osteoclast generation and bone loss is detected. The influence of the farrerol on the inflammatory bowel disease is analyzed through 16S rRNA (ribosomal Ribonucleic Acid) sequencing. In addition, PI3K-AKT and MAPK signal pathways related to osteoclast differentiation of the farrerol are detected through WB and PCR experiments to clarify the basic molecular mechanism of the farrerol for preventing osteoporosis. Results show that the farrerol can improve bone loss caused by enteritis, recover intestinal flora composition and repair intestinal barriers.

The invention also related to a device and a computer-implemented method for generating n virtual Immunohistochemical (IHC) stain images from one Hematoxylin and Eosin (H&E) stain image, using the IHC machine learning system obtained thanks to the device for training.

A method for automatic mitosis detection based on data and feature diversity is provided, in which a training-free hematoxylin-based detection approach is employed to obtain candidate samples; a balanced sampling strategy is applied to remove redundant information from the candidate samples, balance the data volume, and maintain sample diversity and eliminate easy samples, so as to obtain balanced and representative training dataset that facilitate a classifier in learning representative features; in view of the morphological complexity of mitotic cells, a jointly trained classifier is designed, in which subclass division is performed based on binary classification, the divided subclasses are used as pseudo-labels, and the classifier is trained with parent-class labels and subclass pseudo-labels to obtain a parent-subclass joint classifier. A system for implementing such method is also provided.

Described herein are computer-implemented methods for analysis of a tissue sample. An example method includes: annotating the whole tumor regions or set of tumorous sub-regions either on a biomarker image or an H&E image (e.g. from an adjacent serial section of the biomarker image); registering at least a portion of the biomarker image to the H&E image; detecting different cellular and regional tissue structures within the registered H&E image; computing a probability map based on the different detected structures within the registered H&E image; deriving nuclear metrics from each of the biomarker and H&E images; deriving probability metrics from the probability map; and classifying tumor nuclei in the biomarker image based on the computed nuclear and probability metrics.

The application relates to the technical field of medical data processing, and discloses a training method of a pathological image recognition model, a pathological image recognition method and device, the training method of the pathological image recognition model comprising the following steps: obtaining a plurality of histopathological image samples; for part or all of the histopathological image samples, converting a color space into an H&E space to obtain an initial H&E space image, the H&E space comprising two color channels, namely a hematoxylin color channel and an eosin color channel; for the initial H&E space image, using a B-spline curve function to perform dyeing enhancement processing or dyeing weakening processing on at least one color channel to obtain a target H&E space image; and using the histopathological image samples and the target H&E space image to train a pre-constructed histopathological image recognition model. The application can improve the generalization ability of the histopathological image recognition model, and further improve the accuracy.

The application discloses a colorectal cancer HER2 state classification method based on a double-branch cooperative optimization network and belongs to the technical field of pathological image processing. The application adopts a double-path cooperative optimization framework, combines knowledge transfer and cross-modal feature re-projection, efficiently extracts features related to the HER2 state from a conventional hematoxylin-eosin (H&E) staining section, and can directly accurately judge the HER2 state. Through the prior knowledge transfer and cross-modal feature re-projection mechanism, the application breaks through the bottlenecks such as insufficient utilization of prior knowledge and modal heterogeneity interference in the traditional method, and significantly improves the accuracy and robustness of the colorectal cancer HER2 state classification.

To provide a hematoxylin formulation comprising racemic hematoxylin.SOLUTION: A first aspect of the present disclosure is a hematoxylin formulation including racemic hematoxylin and at least one additive (e.g., at least one additive selected from a solvent, a chemical oxidant, a mordant, a stabilizer, and an antioxidant). Another aspect is a method for staining a biological sample, comprising contacting the biological sample with a racemic hematoxylin formulation. In some embodiments, the racemic hematoxylin formulation includes a solvent, hematoxylin, an amount of a chemical oxidant sufficient to convert at least a portion of the hematoxylin into hematein, a mordant, an antioxidant, and a stabilizer. In some embodiments, the stabilizer is selected from the group consisting of a polysaccharide, a cryptand, a cryptophane, a cavitand, a crown ether, a dendrimer, a nanotube, a calixarene, a valinomycin, and a nigericin.SELECTED DRAWING: None

The invention relates to an immunohistochemical detection reagent and kit for bladder cancer neuroendocrine subtype diagnosis and prognosis based on a PHF6 index. The reagent comprises a PHF6 primary antibody, a PHF6 secondary antibody, an antigen repair solution, blocking serum, a DAB developing solution, a hematoxylin staining solution, a hydrochloric acid-alcohol differentiation solution, an ammonia water blue-returning solution and a neutral gum mounting medium. The obvious correlation between the expression level of the PHF6 and the neuroendocrine subtype of the bladder cancer is determined by detecting the expression condition of the PHF6 in the bladder cancer tissue and combining a series of cell experiments and clinical pathological analysis. The invention provides a novel and effective diagnosis means which can help a clinician to accurately judge the neuroendocrine subtype of bladder cancer, so that a more accurate individualized treatment scheme is formulated for a patient, and more reliable prognosis evaluation is provided.

Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.

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.

The invention belongs to the technical field of artificial intelligence and digital pathology crossing, and relates to a regulatory T cell infiltration prediction method based on a digital pathological section. According to the method, a conventional hematoxylin-eosin dyed full-section digital image is used as input, and an analysis range is limited through tissue region segmentation and tumor region positioning; extracting multi-scale morphological characteristics of the visual field units in the tumor area, and constructing a graph neural network coding local-global space context; on the premise of taking an adjacent section immunohistochemical quantification or transcriptome immune deconvolution result as a supervision signal, improving the robustness of a cross-staining batch and a cross-scanning condition by adopting regression learning in combination with adversarial regularization and transfer learning; and outputting regulatory T cell infiltration density or grade and thermodynamic diagram visualization, and further giving uncertainty and spatial heterogeneity indexes for review and decision support.

A machine learning predictor model is trained to generate a prediction of the appearance of a tissue sample stained with a special stain such as an IHC stain from an input image that is either unstained or stained with H&E. Training data takes the form of thousands of pairs of precisely aligned images, one of which is an image of a tissue specimen stained with H&E or unstained, and the other of which is an image of the tissue specimen stained with the special stain. The model can be trained to predict special stain images for a multitude of different tissue types and special stain types, in use, an input image, e.g., an H&E image of a given tissue specimen at a particular magnification level is provided to the model and the model generates a prediction of the appearance of the tissue specimen as if it were stained with the special stain. The predicted image is provided to a user and displayed, e.g., on a pathology workstation.

A method of displaying biological image data in an interface application on a digital pathology analysis system includes: receiving a first user input pertaining to a first patient case; contacting a remote server for one or more stored biological image tiles corresponding to the received first user input, wherein the stored biological image tiles are derived from scans of microscope slides having one or more biological samples disposed thereon, the biological samples stained with hematoxylin and eosin or stained to identify presence of one or more biomarkers; receiving location information for the one or more stored biological image tiles corresponding to the first user input; retrieving the one or more stored biological image tiles pertaining to the first user input based on the received location information; and visualizing the retrieved one or more biological image tiles pertaining to the first user input.

A method of displaying biological image data in an interface application on a digital pathology analysis system includes: receiving a first user input pertaining to a first patient case; contacting a remote server for one or more stored biological image tiles corresponding to the received first user input, wherein the stored biological image tiles are derived from scans of microscope slides having one or more biological samples disposed thereon, the biological samples stained with hematoxylin and eosin or stained to identify presence of one or more biomarkers; receiving location information for the one or more stored biological image tiles corresponding to the first user input; retrieving the one or more stored biological image tiles pertaining to the first user input based on the received location information; and visualizing the retrieved one or more biological image tiles pertaining to the first user input.

Methods for dyeing suture material that include incubating the suture material in a high temperature staining bath comprising hematein are disclosed. The staining bath may include a nonionic surfactant. The method is carried out in the absence of chromium salts. Also disclosed are (i) a dyed suture material produced by the methods and (ii) a surgical suturing kit that includes the dyed suture material.

The invention discloses a pathological section digital virtual staining method and system of a CyclGAN-based unsupervised model, and belongs to the field of digital pathology and artificial intelligence data processing. By combining multi-modal information, an autofluorescence image and a DAPI image of an unstained tissue section are used as input and are converted into a traditional hematoxylin-eosin stained output image for pathological diagnosis. According to the method, an unsupervised model is utilized, dependence on a pixel-level matching training set is avoided, and limitation of a traditional supervised model is broken through. Experimental results show that the generated virtual dyeing image is excellent in dyeing consistency and contrast, and the accuracy and efficiency of clinical diagnosis can be improved. Besides, a conventional fluorescence microscope is adopted in the method, so that the use of chemical reagents and complex experimental equipment is avoided, and a relatively high practical value is provided for clinical transformation of a virtual dyeing technology.

The application belongs to the technical field of artificial intelligence and digital pathology, and relates to a regulatory T cell infiltration prediction method based on digital pathological sections. The method takes a conventional hematoxylin-eosin staining whole section digital image as input, limits the analysis range through tissue region segmentation and tumor region positioning, extracts multi-scale morphological features on a field unit in the tumor region, and constructs a graph neural network to encode local-global spatial context. Under the premise of taking adjacent section immunohistochemical quantification or transcriptome immunodeconvolution results as a supervision signal, regression learning is adopted, combined with adversarial regularization and transfer learning to improve the robustness of cross-staining batches and cross-scanning conditions. The output is the regulatory T cell infiltration density or grade and a heat map visualization, and further uncertainty and spatial heterogeneity indicators can be given for review and decision support.

The invention provides a tumor molecular typing recognition system and method based on a neural network. The method comprises the steps that A1, a full-view digital pathological section of a to-be-recognized hematoxylin-eosin dyed smooth muscle tumor is obtained to serve as a to-be-recognized section; a2, preprocessing the to-be-identified slices to obtain to-be-identified image data formed by preprocessing; and step A3, inputting the to-be-identified image data into a pre-trained molecular typing identification model, and processing the to-be-identified image data by the molecular typing identification model to obtain a typing identification result of the smooth muscle tumor molecules. A CNN-based deep learning mode is established to identify smooth muscle tumor molecular typing, the accuracy is high, the operation is simple, the consumed time is short, the human input is low, the capital cost is low, meanwhile, the requirement for pathological diagnosers is low, and the method can be widely applied to conventional pathological diagnosis of hospitals at all levels, so that LMS patients are benefited for a long time.

A computer-implemented method (530), system (100, 201), and computer-readable medium for generating a slide-free histology image in the form of a reconstructed ultraviolet-based autofluorescence microscopy (UV-AtuoM) image from a plurality of speckle-illuminated low-resolution images. A computer-implemented method (1150), system (100, 201), and computer-readable medium for generating a slide-free histology image in the form of a pseudo-hematoxylin and eosin (H&E)-stained image (1120) from a grayscale input image, such as a UV-AutoM image or an ultraviolet-based photoacoustic microscopy (UV-PAM) image (1110).

The present invention discloses the use of a photocurable adhesive for inducing long-lasting emission from organic dyes. The photocurable adhesive is a mixture of one or more of UV-curable adhesive 3491, UV-curable adhesive 3492, UV glue D-606, and UV shadowless adhesive V-3218. The organic dye is a mixture of one or more of safranin, basic fuchsin, Nile blue, hematoxylin, acid fuchsin, fluorescein, and solvent green 7. By mixing the photocurable adhesive with a conventional organic dye, the present invention can induce long-lasting emission from the conventional organic dye.

The invention belongs to the field of medicines, and particularly relates to application of bitter almond alcohol A in preparation of a product for preventing and treating osteoporosis. The method comprises the following steps: firstly, adopting tartrate-resistant acid phosphatase staining, cell viability analysis, western blot and the like at a cellular level to explore the influence of the bitter apricot alcohol A on osteoclast generation and a molecular mechanism, and then adopting a biological membrane interference technology and SPR to explore the interaction between AA and RANKL and RANK and the influence of the bitter apricot alcohol A on the combination of RANKL and a receptor thereof. And finally, on an ovariectomized mouse osteoporosis model, hematoxylin-eosin staining, TRAP staining, real-time fluorescent quantitative PCR and enzyme-linked immunosorbent assay are adopted to verify the improvement effect of the bitter almond alcohol A on ovariectomized mouse bone loss. According to the application, the biological activity of the bitter almond alcohol A for resisting osteoclast generation is proved for the first time, and a basis is provided for developing the bitter almond alcohol A as a potential medicine for treating osteoporosis.

Methods for dyeing suture material that include incubating the suture material in a high temperature staining bath comprising hematein are disclosed. The staining bath may include a nonionic surfactant. The method is carried out in the absence of chromium salts. Also disclosed are (i) a dyed suture material produced by the methods and (ii) a surgical suturing kit that includes the dyed suture material.

The invention discloses application of cryptotanshinone in preparation of a medicine for treating vascular endothelial cell injury, and belongs to the technical field of medicines. The application of the cryptotanshinone in preparing the medicine for treating the vascular endothelial cell injury is provided for the first time, a new treatment scheme is provided for the vascular endothelial cell injury, and the pharmaceutical application of the cryptotanshinone is expanded. According to the application disclosed by the invention, by culturing human umbilical vein endothelial cells, the influence of the P. gingivalis-sourced lipopolysaccharide on an HUVEC transcriptome is evaluated; constructing an in-vivo lung injury rat model, and utilizing hematoxylin amp; the protection effect of cryptotanshinone on pulmonary edema and pathological injury of rats is evaluated through methods of porphyromonas gingivalis infection, eosin staining, blood gas analysis and the like, and an experimental basis is provided for vascular endothelial cell injury and dysfunction caused by porphyromonas gingivalis infection.

Histologic classification of pathology specimens through machine learning is a nascent field which offers tremendous potential to improve cancer medicine. Its utility has been limited, in part because of differences in tissue preparation and the relative paucity of well-annotated images. We introduce tissue recognition, an unsupervised learning problem analogous to human face recognition, in which the goal is to identify individual tumors using a learned set of histologic features. This feature set is the “tissue fingerprint.” Because only specimen identities are matched to fingerprints, constructing an algorithm for producing them is a self-learning task that does not need image metadata annotations. Here, we provide an algorithm for self-learning tissue fingerprints, that, in conjunction with color normalization, can match hematoxylin and eosin stained tissues to one of 104 patients with 93% accuracy. We applied this identification network's internal representation as a tissue fingerprint for use in predicting the molecular status of an individual tumor (breast cancer clinical estrogen receptor (ER) status). We describe a fingerprint-based classifier that predicts ER status from whole-slides with high accuracy (AUROC=0.90), and is an improvement over traditional transfer learning approaches. The use of tissue fingerprinting for digital pathology as a concise but meaningful histopathologic image representation enables a new range of machine learning algorithms leading to increased information for clinical decision making in patient management.

The present disclosure provides stabilized hematoxylin formulations having a pH of less than 2.4. The present disclosure also provides methods of using such stabilized hematoxylin formulations to stain biological samples.

The present application relates to the field of pathological detection technology, in particular to a reagent for staining pathological sections and its application. A reagent for staining pathological sections, including hematoxylin staining solution, the hematoxylin staining solution includes the following raw materials: 2-3g hematoxylin, 17-18g potassium aluminum sulfate, 0.15-0.25g sodium iodate, 240-260ml anhydrous ethanol, 700-800ml distilled water, 8-12ml glacial acetic acid, 0.1-0.2g hybrid hydrogel; the hybrid hydrogel is prepared by grafting amino clay on cyclodextrin. The present application adjusts the ratio of each raw material, makes full use of the oxidation and acid-base balance of the reagent itself, ensures the stability of the reagent, and adds a hybrid hydrogel to quickly adsorb iodine with a negative charge, reduces the influence of the reduction product of sodium iodate on staining, shortens the staining time, and improves the color development of the staining.