77 results about "Histology" patented technology

A tamper for making paraffin blocks for histology may include a pliable tamper head and a handle. The handle may be permanently or removably attached to the tamper by using screws, magnets, or the like. The tamper may be made from a pliable material, such as a silicone rubber, with improved thermal transfer and high latent heat. In use, the tamper may be warmed in advance to a working temperature by placing the tamper on a hot plate of the embedding station or on a dedicated smaller hot plate. Alternatively, the tamper may be heated with an electric resistive element, a thermoelectric device, or an electric induction coil.

The invention provides a space transcriptomics organization space domain intelligent identification method based on a Graph Transform. The method comprises the following steps: reading a space transcriptomics data file; extracting features in the histological image data by adopting a pre-trained deep learning model; setting a spatial distance threshold value; acquiring spatial position data of each sampling point according to a set spatial distance threshold value, and constructing an adjacent matrix; combining the gene expression data, the adjacent matrix and the histological image features to obtain a normalized feature matrix; performing dimensionality reduction on the normalized feature matrix by adopting PCA; inputting the feature matrix subjected to dimension reduction processing into a feature learning model to obtain a comprehensive feature representation; clustering the obtained feature representations by adopting a Leiden algorithm to obtain a spatial domain classification result; according to the invention, deep fusion of multi-modal information is realized through a Graph Transform architecture, spatial distribution rules and functional characteristics of cells can be captured at the same time, and a systematic solution is provided for analyzing a complex structure of a tissue microenvironment.

1. Name of the designed product: upper sheet scanner. 2. Use of the designed product: the designed product is used for cell scanning (manual upper sheet) of histological specimens. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

The utility model discloses a pathology and histology cytopathy observation device, which relates to the technical field of pathology and histology and comprises a box body, an electron microscope is fixedly mounted at the bottom of the rear side in the box body, and a rotating shaft is rotatably mounted between the top and the bottom of the front side in the box body. A placement box is fixedly mounted below the outer surface of the rotating shaft, the top of the placement box is of an opening structure, a plurality of placement grooves are circumferentially formed in the placement box at equal intervals, and one side of the placement box extends to the position below a lens of the electron microscope; and a driving mechanism is fixedly mounted at the top of the box body. According to the utility model, the glass slides can be automatically replaced, manual one-by-one replacement is not needed, the operation time is reduced, sample pollution is avoided, and the box body can effectively avoid pollution of a microscope lens or foreign matters attached to the surfaces of the glass slides caused by dust or airflow in an external environment, so that the imaging definition is reduced, and normal observation of cytopathy is influenced.

A histologic tissue sample support device (600) includes a tissue cassette (616), a cassette feature (626), and a frame (612). The tissue cassette (616) has at least one side wall (616a) and is coupled to the cassette feature (626). The cassette feature (626) has at least one element to secure, align, orient, bias, and / or provide indicia to identify a tissue sample. The tissue cassette (616) is movably coupled to the frame (612) that has a bottom edge (612f). The tissue cassette (616) and the cassette feature (626) are capable of moving from a first position to a second position with respect to the frame (612) and, in the second position, at least a portion of the cassette feature (626) and at least a portion of the side wall (616a) extend beyond the bottom edge (612f) of the frame (612) for sectioning in the microtome.

Disclosed are systems and methods for an artificial intelligence based pathology platform that can provide prognostic value to clinicians. For example, the platform can predict outcomes related to a cancer, and may include the steps of obtaining a histological sample of a cancer tumor of a patient, determining a feature set for the histological sample by applying a deep learning module trained on a population of histological samples of cancer tumors of the same type as the obtained histological sample of the cancer tumor, and generating an outcome set for the patient by applying a second model to the determined feature set.

There is provided a method for quantifying texture features in histological sample from a tumor sample, comprising:receiving a digital image of the histological sample, thendividing the digital image into a plurality of sub-areas, thenusing a trained machine learning model to predict a presence of at least one biological feature for each of the sub-areas, where a probability for the presence of the biological feature is represented by a value, thenforming a data matrix by arranging the values for the probabilities of the biological features in the same way as the sub-areas are arranged in relation to the digital image, thenapplying image analysis to the data matrix for a set of texture features, to produce a quantification of at least one texture feature.

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. Here, we used molecular profiling data to identify biomarker signatures (biosignatures) that predict a tumor primary lineage, cancer category or type, organ group and / or histology. The signature may use genomic and transcriptome level information.

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.

The application discloses a kind of food relieving bone joint damage evaluation method, it is related to food efficacy evaluation technical field, for solving the problem of single dimension of existing evaluation method, unstable model leads to poor reliability of conclusion.The application builds composite modeling and multi-modal analysis mechanism, first, a stable animal model is established by using chemical injury combined with forced exercise training, a two-stage increasing dosing regimen is set, and behavior dynamic trend data and histology, imaging multi-modal results are collected synchronously.Then, based on the weight assigned to osteoarthritis stage, a weighted composite score is generated, and a perturbation sensitivity index is calculated, a multi-index collaborative influence map is constructed, a key evaluation node is located, a graded evaluation conclusion is generated based on the food action characteristics and efficacy determination threshold, and false positives caused by single index fluctuations are avoided.Finally, a full-cycle closed-loop evaluation process is formed, which improves the scientificity, reproducibility and self-adaptive ability of efficacy determination in complex biological systems.

Histology laboratories in hospitals and elsewhere require significant amounts of manual work. These labs also have to contend with rising workloads and increasing work complexity. Current labor vacancy rates are about 10-12%. and it has been predicted that the retirement rate in these work environments will be about 18-36% within the next 5 years. Within these labs, microscope slide cleaning in preparation for scanning and / or pathologist review is often a tedious task. Typically, the slide cleaning process requires manual application of cleaning fluids and wiping of slides with lens paper to remove dust, fingerprints, specks, and the like. Leaving these contaminants on the slide can lead to out of focus image sections which increases the risk of obscuring diagnostically relevant information. Thus, there is a need for improvement in this field.

The application provides a kind of intelligent biopsy area prompting method and system for digestive endoscopy, it is related to artificial intelligence auxiliary diagnosis and treatment field, including: obtaining real-time digestive endoscopy video stream;Through multi-scale visual neural network, multi-scale feature extraction is carried out frame by frame to video stream, candidate biopsy area detection is carried out based on multi-scale feature;Biological histology level multi-feature fusion analysis is carried out to candidate biopsy area, and the biopsy value of candidate biopsy area is evaluated;Based on biopsy value, generate prompt element, and real-time superimposed in original video stream;Wherein, the biological histology level multi-feature fusion analysis is from color, texture, blood vessel, morphology, semantic multi-feature of candidate biopsy area is constructed, and the confidence of multi-feature and candidate biopsy area is used to score biopsy value.The application can accurately prompt potential biopsy area in the process of examination, so as to assist doctors to improve sampling scientificity and diagnostic accuracy.

Features are disclosed for imaging block and slice samples using an imaging system. The imaging system can link the images by identifiers associated with the block and slice samples. The imaging system can train a machine learning algorithm based on correctly linked images. In some embodiments, the trained machine learning algorithm may include an image analysis module or a convolutional neural network. The imaging system can use the trained machine learning algorithm in order to determine a confidence score of a match between the block and the slice samples. The trained machine learning algorithm can use features of the block and the slice samples such as shape and tissue morphology to determine whether the samples match. In some embodiments, when the confidence score is below a certain threshold, the imaging system can alert a user that the samples may not match.

The method may include determining a first plurality of tiles having a first tile size and a second plurality of tiles having a second tile size within an image of the biological sample. A feature extraction model may be applied to extract features from tiles of different sizes. Connected feature sets may be formed, each including a first feature of a first tile from the first plurality of tiles, a second feature of a second tile from the second plurality of tiles, and a third feature of a third tile from the second plurality of tiles. Molecular features present in the biological sample may be determined based on an attention-weighted positional embedding of the connected feature sets and a joint representation of features across clusters of spatially adjacent tiles within the image. Related systems and computer program products are also provided.

A method and system for classifying field of view (FOV) images of histological slides into various categories that include certain stain patterns, artifacts, and / or other features of interest are provided herein. Few-shot learning (e.g., a prototypical network) techniques are used to train a deep convolutional neural network using a small number of training samples for a small number of image classes for classifying stain images belonging to a larger number of image classes.

This invention relates to the field of medical image processing technology and discloses an adaptive feature processing method for pathological slide images of gastroenteritis. The method includes constructing a multi-scale feature analysis model based on the histological features of the gastroenteritis pathological slides to extract texture and morphological features at different tissue levels in the pathological slides; then constructing an adaptive feature optimization strategy to dynamically adjust feature selection parameters based on the feature data output by the multi-scale feature analysis model; next, performing joint calculations on the multi-scale feature analysis model and the adaptive feature optimization strategy according to the selected pathological slide sample type to obtain an optimized feature dataset; finally, establishing a feature representation of the gastroenteritis pathological slide image based on the optimized feature dataset. This method can comprehensively extract pathological features, dynamically optimize feature selection parameters, improve the quality of the feature dataset and the accuracy of feature representation, adapt to different sample types, and meet the needs of pathological analysis.

A computational pathology method includes receiving multi-parameter cellular and / or sub-cellular imaging data for an image of a tissue sample, and locating and segmenting a plurality of tissue components of the tissue sample in the multi- parameter cellular and sub-cellular imaging data to generate segmented multi¬ parameter cellular and sub-cellular imaging data. The method further includes applying a parametric feature modelling scheme to certain of the tissue components in the segmented multi-parameter cellular and sub-cellular imaging data, wherein the parametric feature modelling scheme is generated from a dictionary of pre-existing diagnostically relevant histological patterns and comprises a number of structural features adapted for defining a number of disease entities of a disease, and wherein the applying includes determining a quantification of each of the structural features for the tissue sample, and classifying a state of the disease in the tissue sample based the determined quantification of each of the structural features.

Diagnosis of prostate cancer often involves invasive measures, such as biopsies, in which tissue is removed and graded by a pathologist. Non-invasive measures, such as multi-parametric MRI, can be used to examine lesions and grade them using the PI-RADS scoring scale; however, achievable image resolution is limited by motion. A new paradigm, magnetic resonance histopathology (MRH), is disclosed, which aims to evaluate tissue texture at sub-millimeter resolution through a fast clinical acquisition process. The ability of MRH to identify cancerous tissue in the prostate through computer simulation analysis is demonstrated, reproducing the MRH measurement process on a set of high-resolution, histology slides annotated by pathologists (2, 4, 6, 8, 10, 12, 14). A dataset of spectral intensities (20) at sub-millimeter wavelengths is created, and a deep learning model (22) trained to classify the spectral data is based on normal or tumor tissue. A set of spatial frequencies is identified, which is used to optimize diagnostic capability under the constraint of limited acquisition time. In addition to single-region classification, the disclosed method and architecture integrate spatial context and local information, the inclusion of which improves model performance and denoises inference results. In addition to algorithms for estimating the physical length scale of lesions identified by the model, the trained model is demonstrated to be applied to unlabeled high-resolution 3D MRI data.

A uniquely identifiable consumable product for use in a histology or cellular pathology process for processing a biological sample from a source, the consumable product having a unique identifier which is not linked to the source of the sample is provided. By applying a unique identifier to cassettes and other consumable products prior to use, laboratory printers for printing identification information on tissue processing cassettes and on microscope slides and the like are redundant. The unique identifier is then linked to information relating to the sample to be analysed and its source. The physical process of applying a unique identifier to the consumables prior to use enables the decoupling of the physical printing of the consumable from information relating to the source of the sample obviates the need for provision of a printing capability for consumables in laboratories.

The invention discloses a liver cancer iconography diagnosis method based on artificial intelligence, which integrates anatomical structure characteristics of CT enhanced scanning, soft tissue heterogeneity characteristics of MRI diffusion weighted imaging and dynamic blood flow characteristics of ultrasound contrast, and covers three dimensions of morphology, histology and hemodynamics of a focus. The one-sidedness defect of single-mode information is overcome, multi-mode data complementation is achieved, the misdiagnosis and missed diagnosis risks caused by feature missing are effectively reduced, and the diagnosis accuracy is improved. The cross-modal attention module calculates the cosine similarity between each modal and the global reference feature and dynamically distributes the weight, thereby avoiding the limitation of the fixed weight, enabling different modal features to make accurate contributions according to the actual expression of the focus, strengthening the characterization capability of the combined feature for the liver cancer focus, and improving the accuracy of the liver cancer focus. And the recognition precision of the model on difficult focuses such as fuzzy boundaries and complex blood supply is improved.

Angiographic examination procedure of an object of study (6, 13) to determine morphology, histology and / or condition of moving walls of vessels (15, 20) comprising the following steps: S1 Acquisition of a series (13, 16) of angiographic images (14, 17) of a section of interest (20) of a vessel (15, 20), S2 quantitative analysis of the vessel wall (21) of the section (20) of the vessel (15, 20), S3 Calculation of the intrinsic movement (26, 27) of the vessel wall (21) in relation to expansion and contraction of the vessel (15, 20) from each of two consecutive angiographic images (14, 17) and S4 Visualization of the difference in intrinsic movement (26, 27) of the vessel wall (21) and / or S5 Visualization of the morphology and / or histology of the vessel wall (21).

The imaging device may be used for histological and / or molecular imaging of tissue samples. The imaging device may include one or more light sources. The one or more light sources generate one or more excitation beams directed to an excitation position focused on the sample to generate signals at the sample, and one or more interrogation beams directed to a detection position, where a portion of the one or more interrogation beams returning from the sample indicates at least some of the generated signals. The imaging device may include a photodetector configured to detect emission signals from the sample. The imaging device may generate an image of the sample using only pressure (photoacoustic) signals, only temperature (photothermal) signals, and / or both photoacoustic and photothermal signals from the generated signals.

A computational pathology method includes receiving multi-parameter cellular and subcellular imaging data of a tissue sample and locating and segmenting a plurality of tissue components of the tissue sample in the multi-parameter cellular and subcellular imaging data to generate segmented multi-parameter cellular and subcellular imaging data, applying a parametric feature modeling scheme to particular tissue components in the segmented multi-parameter cellular and subcellular imaging data, the parametric feature modeling scheme being generated from a dictionary of existing diagnostically relevant histological patterns and including several structural features suitable for defining several disease entities of the disease, applying the parametric feature modeling scheme including determining a quantification of each of the several structural features for the tissue sample, and classifying a disease state in the tissue sample based on the quantification of each of the several structural features.

A method for synthesizing an IHC image based on a histological image of a tissue sample is disclosed. A computer-implemented system generates a first input specifying at least a portion of the histological image and processes the first input using a predictive neural network to generate a predicted score indicative of an expression level of a molecular marker. The system further processes a second input comprising data specifying the predicted score using a generative neural network to generate a synthetic IHC image characterizing a spatial distribution of the molecular marker within the tissue sample. The generative neural network has been trained with a discriminator neural network, the generative neural network and the discriminator neural network being constituent parts of a conditional generative adversarial network (cGAN), the cGAN being conditional to the predicted score.

The invention provides a composite biological scaffold based on chondrocyte microspheres as well as a preparation method and application thereof, and relates to the technical field of biomedical materials. The composite biological scaffold based on the cartilage cell microspheres is prepared by using a process flow combining a microfluidic technology and a biological printing technology, taking the cartilage cell microspheres as a high-activity construction unit and taking light-cured hydrogel as a support carrier. In-vitro experiments show that the scaffold significantly improves cell viability, extracellular matrix deposition and chondrogenesis gene expression level, and meanwhile, the scaffold is stable in form; in-vivo research shows that the scaffold is highly similar to natural auricular cartilage in the aspects of macroscopic morphology, histological characteristics and mechanical strength. The composite biological scaffold provided by the invention not only can effectively promote the regeneration of the elastic cartilage, but also makes up the key defects of the traditional hydrogel scaffold, and provides an innovative strategy with clinical application value for auricle defect repair.

There is provided a method comprisingdetermining cancer progression risk for a cancer patient by providing a digital image to a trained neural network and allowing the trained neural network to predict cancer progression risk for the patient based on that image, where the neural network has been trained byreceiving a training dataset comprising digital images of histology samples from cancer patients where each histology sample is associated in the dataset with one histology grade score selected from a set comprising three histology grade scores: a first histology grade score indicating low risk for progression of the cancer disease, a second histology grade score indicating intermediate risk for progression of the cancer disease and a third histology grade score indicating high risk for progression of the cancer disease,using the digital images of histology samples associated with the first and third histology grade scores, while ignoring digital images associated with the second histology grade score, to train a neural network for determining the cancer progression risk for a patient.

The present application relates to the technical field of digital image processing, in particular to a tumor interstitial proportion quantitative evaluation system based on pathological images. The present application automatically obtains tumor epithelial region, tumor interstitial region, tumor region and tumor infiltration front region based on image segmentation, without pre-selecting histological evaluation position for visual evaluation by artificial selection, avoiding the subjective experience dependence of traditional methods, and improving the efficiency of tumor interstitial proportion evaluation. In addition, the present application comprehensively quantitatively evaluates the first tumor interstitial proportion under the overall scale of the to-be-tested pathological image, the second tumor interstitial proportion under the scale of the tumor infiltration front region and the third tumor interstitial proportion under the scale of the tumor region, and finally obtains the tumor interstitial proportion score value which can reflect the tumor interstitial proportion characteristics of different anatomical positions, further improving the accuracy of the evaluation result. The preset rule is to select the window tumor interstitial proportion with the highest tumor interstitial proportion value.

A supramolecular histochemistry system for staining includes a chaotropic ion and a chaotropic ion complexing agent that acts as a molecular host and accepts the chaotropic ion as a molecular guest. The chaotropic ion facilitates the diffusion of a probe, such as an antibody, into and within a tissue sample, binding the probe to the molecular host or diluting the probe, thereby promoting the association of the antibody with a target antigen and generating an immunostaining or histochemistry signal. Alternatively, a small fluorescent probe serves as the molecular guest and is complexed by the supramolecular host to facilitate deep penetration. A method for performing histology employs the supramolecular system with deep, uniform histochemistry. Such a method is relatively rapid, scalable, automatable, and cost-effective.