77 results about "Haematoxylin" patented technology

The invention provides a bladder cancer prognosis prediction method and system, and relates to the technical field of intelligent detection, and the method specifically comprises the steps: obtaining and preprocessing a bladder cancer hematoxylin-eosin (HE) staining pathological image to obtain an effective color block, and marking to obtain a tumor and necrosis training set; respectively training the two initial models based on the tumor training set and the necrosis training set to correspondingly obtain a tumor model and a necrosis model; acquiring a to-be-predicted bladder cancer pathological image, and performing tumor and necrosis region identification based on the tumor model and the necrosis model to correspondingly obtain a tumor region and a necrosis region; based on the space coordinate information of the tumor area and the necrotic area, carrying out numerical calculation to obtain a space distribution index of the necrotic area relative to the tumor area; and carrying out survival analysis based on the spatial distribution index to obtain survival prognosis information of the bladder cancer patient. The deep learning technology is applied to the prognosis direction of the bladder cancer, a clinician is assisted in evaluating survival prognosis of a patient, and a reference basis is provided for diagnosis and treatment decisions.

A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

The invention discloses a high-resolution breast cancer HER2 scoring method based on deep learning. The high-resolution breast cancer HER2 scoring method comprises the following steps: constructing a full-slide image data set containing hematoxylin-eosin and HER2 staining; accurately marking by a pathological expert, and distinguishing normal, in-situ and invasive tumor areas; cutting, classifying and marking blocks with different sizes and magnification times; carrying out gray level conversion, binarization and morphological operation on the image blocks to extract a tissue region; designing a deep learning model combining a visual converter and Fourier transform, and optimizing feature extraction and frequency domain analysis; an optimal backbone network structure is selected through comparison; the model is trained, parameters are optimized, and the recognition and evaluation accuracy is improved; and finally integrating to an automatic scoring software system to realize automatic identification, HER2 protein expression evaluation and automatic scoring. The system is high in automation degree, the HER2 scoring process is simplified, the auxiliary diagnosis precision is improved, the workload of doctors is reduced, and pathologists are helped to concentrate on complex diagnosis tasks, so that the patient management effect is improved.

A machine learning (ML) workflow for virtual staining and computational pathology based on a label-free tissue sample is provided to avoid a laborious and time-consuming procedure of pathologist-based interpretation of formalin-fixed and paraffin-embedded histology. A virtual-staining ML model translates a label-free autofluorescence (AF) whole slide image (WSI) of the tissue sample into a virtual hematoxylin and eosin (H&E) -stained WSI, which is further transformed into a WSI virtually stained with a special stain by a stain-transformation ML model. Each of the ML models can be realized as a U-Frame model. A multiple instance learning (MIL) network with attention-based pooling can be applied to the virtual H&E-stained WSI or AF WSI to provide fast and accurate tumor diagnosis. Furthermore, the attention-based MIL network can be configured as a clustering-constrained-attention multiple instance learning (CLAM) model for providing multiclass tumor subtyping, or even multiclass disease typing, in diagnosis of the tissue sample.

A machine learning system makes medical predictions based, at least in part, on pathomic features indicating collagen fiber organization extracted from pathology images that include lesions. The pathomic features are extracted from pathology images. The pathology images may be routine clinical images gathered in the course of diagnosis and treatment, such as hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of tissue. The medical predictions may concern, e.g., the diagnosis, prognosis, genotype, or phenotype of a lesion, and may include disease-free survival (DFS) and overall survival (OS) of patients known or suspected of having malignant lesions. Methods for training a machine model to make, and for making, such predictions are also disclosed.

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.

A technique for expansion microscopy (ExM) utilizing anchorable derivatives of hematoxylin and eosin (H&E) stained tissue specimens within tissue samples, in order to achieve high resolution detection of biomolecules with precise morphological features of cells and nuclei, which in turn, allows for effective for accurate early stage disease diagnosis including various cancers. Staining with H&E employs anchorable derivatives that can be cross-linked into the ExM hydrogel matrix.

To provide a system and method of training a model capable of identifying the hormone receptor status of a subject by means of whole slide images (WSIs) of hematoxylin and eosin (H&E) stain of his / her biopsies, and to provide a method of identifying the hormone receptor status of a subject using the above mentioned method and model.SOLUTION: A method disclosed herein comprises a step S101 of acquiring multiple WSIs having known hormone receptor information, a step S102 of dividing each of the WSIs into a plurality of patches, a step S104 of selecting and combining patches expressing abnormal H&E stain to produce a combined image; and a step S105 of training a plurality of combined images independently with the aid of the known hormone receptor information of the WSIs so as to construct a model.SELECTED DRAWING: Figure 1

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.

The invention relates to the technical field of biology, and particularly provides a hematoxylin staining solution and a preparation and staining method thereof.The hematoxylin staining solution is prepared from hematoxylin, absolute ethyl alcohol, aluminum potassium sulfate, sodium iodate, glacial acetic acid and water, the proportion of all the raw materials is adjusted in a targeted mode, the dosage of all the raw materials is strictly controlled, and it is accidentally found that the hematoxylin staining solution can be used for staining hematoxylin. After a tissue slice is stained by the hematoxylin staining solution provided by the invention, cell nucleus coloring is good, a tissue structure is clear, the tissue slice subjected to eosin restaining is bright in color, strong in contrast, easy to observe, ideal in staining effect, high in staining stability, not easy to fade and beneficial to long-term preservation of the slice, the preparation method is simple, and an unexpected effect is achieved.

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.

The invention discloses a pathological picture classification method based on multi-instance learning, which is used for weakly supervised full-section pathological image classification. Firstly, a positive and negative sample pair is constructed by integrating a self-supervised contrast learning strategy, so that different enhanced versions of the same pathological picture are closer in a feature space, the feature distinguishing ability is effectively enhanced, and the performance of the model in a weak supervision environment is improved. Secondly, introducing a solution based on hematoxylin-eosin (Hamp; and E) a dyeing separation data enhancement technology is adopted to generate more diversified training data, so that the generalization ability and robustness of the model are improved. And finally, causal intervention is carried out through backdoor adjustment to reduce the data set deviation, so that the influence of the data set deviation on the model training and reasoning process is effectively reduced, and the reliability of the model is improved. The model can accurately position a pathological area, and powerful technical support is provided for clinical application.

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.