Embodiments disclosed herein generally relate to expression-level prediction for digital pathology images. Particularly, aspects of the present disclosure are directed to accessing a duplex immunohistochemistry image of a slice of specimen, wherein the duplex immunohistochemistry image comprises a depiction of cells associated with a first biomarker and/or a second biomarker corresponding to a disease; generating, from the duplex immunohistochemistry image, a first synthetic image depicting the first biomarker and a second synthetic image depicting the second biomarker; determining a set of features representing pixel intensities of the depiction of cells in the first synthetic image and the second synthetic image; processing the set of features using a trained machine learning model; and outputting a result that corresponds to a predicted characterization of the specimen with respect to the disease based on an output of the processing corresponding to a predicted expression level of the first biomarker and the second biomarker.
A method of predicting overall survivability of a patient by a prediction system based on machine learning includes receiving, by the prediction system including a processor and a memory, a plurality of input modalities corresponding to the patient, the input modalities being of different types from one another, generating, by the prediction system, a plurality of intermediate features based on the plurality of input modalities, each input modality of the plurality of input modalities corresponding to one or more features of the plurality of intermediate features, and determining, by the prediction system, a survivability score corresponding to an overall survivability of the patient based on a fusion of the plurality of intermediate features.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
3.
SYSTEM AND METHOD FOR QUANTIFICATION OF DIGITIZED PATHOLOGY SLIDES
A method of determining a raw score of a pathology slide from a tissue sample includes receiving, by a regression system, a plurality of first slide features corresponding to the pathology slide, calculating, by the regression system, one or more second slide features corresponding to the pathology slide based on the plurality of first slide features, and determining, by the regression system, the raw score based on one or more features of an accumulated feature set including the plurality of first slide features and the one or more second slide features.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G01N 33/574 - Immunoassay; Biospecific binding assay; Materials therefor for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
4.
REPRESENTATIVE DATASETS FOR BIOMEDICAL MACHINE LEARNING MODELS
Embodiments disclosed herein generally relate to representative datasets for biomedical machine learning models. Particularly, aspects of the present disclosure are directed to identifying a representative distribution of characteristics for a disease, generating a dataset comprising a set of biomedical images, wherein the dataset has a distribution of the characteristics that corresponds to the representative distribution of the characteristics for the disease, processing the dataset using a trained machine learning model, and outputting a result of the processing, wherein the result corresponds to a prediction that a biomedical image of the dataset includes a depiction of a set of tumor cells or other structural and/or functional biological entities associated with the disease, the biomedical image is associated with a diagnosis of the disease, the biomedical image is associated with a classification of the disease, and/or the biomedical image is associated with a prognosis for the disease.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
5.
ARCHITECTURE-AWARE IMAGE TILING FOR PROCESSING PATHOLOGY SLIDES
Techniques are described herein for architecture-aware image tiling for processing pathology slides. In a particular aspect, a computer-implemented method is provided that includes accessing an image, generating a tiling element for the image based on (i) a number of down-sampling layers to be implemented in a machine learning model, (ii) a size of a kernel to be applied during convolution operations in the machine learning model, (iii) a number of convolutional layers being implemented by the machine learning model at each level or each resolution, or any combination thereof, extracting tiles from the image using the tiling element, inputting each tile into the machine learning model, generating, for each tile, a convolved portion of the image using at least the convolutional layers, the kernel, and the down-sampling layers, generating a convolved version of the image using the convolved portions of the image, and outputting the convolved version of the image.
The present disclosure relates to techniques for efficient development of initial models and efficient model update and/or adaptation to a different image domain using an adaptive learning framework. For efficient development of initial models, a two-step development strategy may be performed as follows: Phase 1: Model preconditioning, where an artificial intelligence system leverages existing annotated datasets and improves learning skills through training of these datasets; and Phase 2: Target-model training, where an artificial intelligence system utilizes the learning skills learned from Phase 1 to extend itself to a different image domain (target domain) with less number of annotations required in the target domain than conventional learning methods. To efficiently perform model update and adaptation to new datasets after initial model development, a digital pathology scenario is identified, an adaptive-learning method is selected based on the scenario, and the model is updated and adapted to new datasets using the adaptive-learning method.
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
Techniques described herein include, for example, generating a feature map for an input image, generating a plurality of concentric crops of the feature map, and generating an output vector that represents a characteristic of a structure depicted in a center region of the input image using the plurality of concentric crops. Generating the output vector may include, for example, aggregating sets of output features generated from the plurality of concentric crops, and several methods of aggregating are described. Applications to classification of a structure depicted in the center region of the input image are also described.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Embodiments described herein pertain to systems and methods for imaging. An imaging system may include a castellated optical element, a CMOS image sensor, and color filtering elements. The CMOS image sensor may include focus areas, a line-scan area, a 2D imaging area, and look-ahead gaps. The imaging system may be configured scan and capture bi-directionally, forward-focused brightfield and fluorescence images of one or more slides comprising at least one biological material.
A slide carrier includes: a base support; and a slide platform having a surface that is parallel to a first plane defined by a first vector and a second vector, wherein a vector extending in a direction opposite to the direction of gravity is normal with respect to a second plane defined by a third vector and a fourth vector, an angle between the first vector and the third vector is greater than zero degrees and less than 90 degrees, and an angle between the second vector and the fourth vector is greater than zero degrees and less than 90 degrees.
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH (USA)
Inventor
Ahanonu, Eze, O.
Mcmahon, Sara, M.
Shanmugam, Kandavel
Sinicrope, Frank, A.
Williams, Crystal, Sue
Yan, Dongyao
Zhang, Wenjun
Abstract
Scoring functions for predicting response of a dMMR and/or MSI-H colorectal tumor to a PD-1 axis-directed therapy are disclosed, as well as methods and systems for evaluating tissue samples for the presence of feature metrics useful in computing such scoring functions. The scoring functions integrate one or more spatial relationships between cell types into a numerical indication of the likelihood that the tumor will respond to the PD-1 axis-directed therapy. Based on the output of the scoring function, a subject may then be selected to receive a PD-1 axis-directed therapy (if the scoring function indicates a sufficient likelihood of positive response) or an alternative therapy (if the scoring function indicates an insufficient likelihood of positive response).
Methods and systems are provided to perform actions including: receiving an image and displaying the image using a graphical user interface; receiving at least one first image annotation provided by a user via the graphical user interface; producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation; and displaying the first segmented image using the graphical user interface; receiving at least one second image annotation provided by the user via the graphical user interface; producing a second segmented image using the deep learning model, wherein the deep learning model uses the digital pathology image, the at least one first image annotation, and the at least one second image annotation; and displaying the second segmented image using the graphical user interface.
A method of depigmenting melanin-pigmented samples is provided. The sample is incubated in the presence of a hydrogen peroxide-based solution at a temperature less than 65 °C for up to 180 minutes. Times, temperatures, and concentrations of hydrogen peroxide that appropriately balance extent of depigmentation with maintenance of cellular morphology and sample retention are also disclosed.
The present disclosure relates to techniques for pre-processing training data, augmenting training data, and using synthetic training data to effectively train a machine learning model to (i) reject adversarial example images, and (ii) detect, characterize and/or classify some or all regions of images that do not include adversarial example regions. Particularly, aspects of the present disclosure are directed to receiving a training set of images for training a machine learning algorithm to detect, characterize, classify, or a combination thereof some or all regions or objects within the images, augmenting the training set of images with synthetic images generated from one or more adversarial algorithms to generate augmented batches of images, and train the machine learning algorithm using the augmented batches of images to generate a machine learning model configured to detect, characterize, classify, or a combination thereof some or all regions or objects within new images.
A multiplex image is accessed that depicts a particular slice of a particular sample stained with two or more dyes. Using a Generator network, a predicted singleplex image is generated that depicts the particular slice of the particular sample stained with each of the expressing biomarkers. The Generator network may have been trained by training a machine-learning model using a set of training multiplex images and a set of training singleplex images. Each of the set of training multiplex images depicted a slice of a sample stained with two or more dyes. Each of the set of training singleplex images depicted a slice of a sample stained with a single dye. The machine-learning model included a Discriminator network configured to discriminate whether a given image was generated by the Generator network or was a singleplex image of a real slide. The method further includes outputs the predicted singleplex image.
Method and systems for of using a machine-learning model to detect predicted artifacts at a target image resolution are provided. A machine-learning model trained to detect artifact pixels in images at a target image resolution is accessed. An image depicting at least part of the biological sample at an initial image resolution can be converted at the target image resolution. The machine-learning model is applied to the converted image to identify one or more artifact pixels from the converted image. Method and systems for training the machine-learning model to detect predicted artifacts at the target image resolution are also provided.
Duplex immunohistochemistry (IHC) staining of tissue sections allows simultaneous detection of two biomarkers and their co-expression at the single-cell level, and does not require two IHC stains and additional registration to identify co-localization. Duplex IHC are often difficult for human including pathologists to reliably score. The methods and system herein use machine-learning models and probability maps to detect and record individual phenotype ER/PR.
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
18.
DIGITAL SYNTHESIS OF HISTOLOGICAL STAINS USING MULTIPLEXED IMMUNOFLUORESCENCE IMAGING
Techniques for obtaining a synthetic histochemically stained image from a multiplexed immunofluorescence (MPX) image may include producing an N-channel input image that is based on information from each of M channels of an MPX image of a tissue section, where M and N are positive integers and N is less than or equal to M; and generating a synthetic image by processing the N-channel input image using a generator network, the generator network having been trained using a training data set that includes a plurality of pairs of images. The synthetic image depicts a tissue section stained with at least one histochemical stain. Each pair of images of the plurality of pairs of images includes an N-channel image, produced from an MPX image of a first section of a tissue, and an image of a second section of the tissue stained with the at least one histochemical stain.
Techniques for image segmentation of a digital pathology image may include accessing an input image that depicts a section of a tissue; and generating a segmentation image by processing the input image using a generator network, the generator network having been trained using a data set that includes a plurality of pairs of images. The segmentation image indicates, for each of a plurality of artifact regions of the input image, a boundary of the artifact region. At least one of the plurality of artifact regions depicts an anomaly that is not a structure of the tissue. Each pair of images of the plurality of pairs includes a first image of a section of a tissue, the first image including at least one artifact region, and a second image that indicates, for each of the at least one artifact region of the first image, a boundary of the artifact region.
The present disclosure provides systems (200) and methods which facilitate the prediction of an estimated time in which one or more fluids will optimally be diffused into a biological specimen, e.g., a tissue sample derived from a human subject. In some embodiments, the present disclosure provides systems (200) and methods which facilitate the prediction of an estimated time until a biological specimen will optimally be fixed with one or more fixatives. In other embodiments, the prediction of a future time at which the biological specimen will be optimally fixed is based on time-of-flight data acquired at a particular point in time during the fixation of the biological specimen that has been deemed sufficiently accurate to predict the time at which the biological specimen will be optimally diffused with fixative.
Disclosed are systems and methods for labelling one or more morphological markers in a biological sample that are characteristic of one or more molecular features. In particular, system and methods are described for labelling one or more morphological markers in a biological sample with covalently deposited narrow band detectable moieties. Narrow band detectable moiety labelling of the one or more morphological markers permits higher order multiplexed assays due to conservation of available spectral bandwidth. Furthermore, as compared to conventional counterstaining methods, covalent deposition of one or more detectable moieties can provide flexibility and robustness with regard to the order in which biomarkers and morphological markers are labeled in a given staining protocol.
Described herein are methods and systems for determining gene alteration states from pathology images. Also described are methods of selecting a treatment for a medical disease, and treating a patient in need thereof, by determining gene alteration states from pathology images. The disclosed methods and systems may also be used to investigate and identify biomarkers corresponding to gene alteration statues of interest.
A method for analyzing an image of a tissue section may include obtaining a plurality of image locations, each corresponding to a different one of a plurality of biological structures; obtaining a plurality of locations of a first biomarker in the image; and calculating a distance transform array for at least a portion of the image that includes the plurality of seed locations. The method may include, for each of the plurality of seed locations and based on information from the first distance transform array, detecting whether the first biomarker is expressed at the seed location, and storing, to a data structure associated with the seed location, an indication of whether expression of the first biomarker at the seed location was detected. The method may include detecting, based on the stored indications, co-localization of at least two phenotypes in at least a portion of the tissue section.
The present disclosure relates to techniques for obtaining a synthetic immunohistochemistry (IHC) image from a histochemically stained image. Particularly, aspects of the present disclosure are directed to accessing an input image that depicts a tissue section that has been stained with at least one histochemical stain; generating a synthetic image by processing the input image using a trained generator network; and outputting the synthetic image. The synthetic image depicts a tissue section that has been stained with at least one IHC stain that targets a first antigen, and techniques may also include receiving an input that is based on a level of expression of a first antigen from the synthetic image and/or generating, from the synthetic image, a value that is based on a level of expression of the first antigen.
The present disclosure is directed to a method of staining a biological specimen (e.g. a single serial tissue section derived from a biological sample) with one or more routine and/or special statins while concomitantly labeling the same biological specimen with one or more detectable moieties without the need for stripping any stain or evaluating different images of stained serial tissue sections of a biological specimen. In some embodiments, the present disclosure is directed to a biological specimen stained with one or more conventional dyes, and where the biological specimen further includes one or more biomarkers labeled with one or more detectable moieties.
Disclosed herein are caged haptens and caged hapten-antibody conjugates useful for facilitating the detection of targets located proximally to each other in a sample.
Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.
Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.
Methods allowing prediction of a response to anti-EGFR therapies are provided, which include histochemical or cytochemical staining methods for staining amphiregulin (AREG) or epiregulin (EREG). Scoring algorithms are provided that may include but are not limited to determining a percent tumor cell positivity for each of EREG and AREG and comparing the determined percent positivity to pre-determined cut offs. The pre-determined cut offs can be either positive cut offs (in which case patients are treated with the EGFR-directed therapy if the percentage is greater than or equal to the cut off), negative cut offs (in which case patients are not treated with the EGFR-directed therapy if the percentage is less than the cut off), or both a positive and negative cut off.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
A method for transferring digital pathology annotations between images of a tissue sample may include identifying a first set of points for a geometric feature of a first image of a section of a tissue sample; identifying a corresponding second set of points for a corresponding geometric feature of a second image of a same tissue sample, the second image being an image of another section of the tissue sample; determining coordinates of the first set of points and coordinates of the second set of points; determining a transformation between the first set of points and the second set of points; and applying the transformation to a set of digital pathology annotations on the first image to transfer the set of digital pathology annotations within the first image to the second image.
Disclosed herein are detectable moieties and detectable conjugates comprising one or more detectable moieties. In some embodiments, the disclosed detectable moieties have a narrow wavelength and are suitable for multiplexing. Also disclosed are methods of labeling one or more targets within a biological specimen using any of the detectable conjugates and/or detectable moieties described herein.
C07D 209/10 - Indoles; Hydrogenated indoles with substituted hydrocarbon radicals attached to carbon atoms of the hetero ring
C07D 279/18 - [b, e]-condensed with two six-membered rings
C07D 311/16 - Benzo [b] pyrans, not hydrogenated in the carbocyclic ring with oxygen or sulfur atoms directly attached in position 2 not hydrogenated in the hetero ring substituted in position 7
C07D 417/04 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and sulfur atoms as the only ring hetero atoms, not provided for by group containing two hetero rings directly linked by a ring-member-to-ring- member bond
C07D 491/22 - Heterocyclic compounds containing in the condensed ring system both one or more rings having oxygen atoms as the only ring hetero atoms and one or more rings having nitrogen atoms as the only ring hetero atoms, not provided for by groups , , or in which the condensed system contains four or more hetero rings
e.g.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.
The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.
Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a slice of specimen. Particularly, aspects of the present disclosure are directed to accessing a multiplex immunofluorescence image of a slice of specimen comprising a first channel for a nuclei marker and a second channel for an epithelial tumor marker, wherein the slice of specimen comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine-learning model; receiving an output of the machine-learning model corresponding to a prediction that the multiplex immunofluorescence image includes one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine-learning model; and outputting the mask for the subsequent image processing.
A machine learning model is accessed that is configured to use one or more parameters to process images to generate labels. The machine learning model is executed to transform at least part of each of at least one digital pathology image into a plurality of predicted labels; and generate a confidence metric for each of the plurality of predicted labels. An interface is availed that depicts the at least part of the at least one digital pathology image and that differentially represents predicted labels based on corresponding confidence metrics. In response to availing of the interface, label input is received that confirms, rejects, or replaces at least one of the plurality of predicted labels. The one or more parameters of the machine learning model are updated based on the label input.
The present disclosure relates to computer-implement techniques for cell localization and classification. Particularly, aspects of the present disclosure are directed to accessing an image for a biological sample, where the image depicts cells comprising a staining pattern of a biomarker; inputting the image into a machine learning model; encoding, by the machine learning model, the image into a feature representation comprising extracted discriminative features; combining, by the machine learning model, feature and spatial information of the cells and the staining pattern of the biomarker through a sequence of up-convolutions and concatenations with the extracted discriminative features from the feature representation; and generating, by the machine learning model, two or more segmentation masks for the biomarker in the image based on the combined feature and spatial information of the cells and the staining pattern of the biomarker.
Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.
A racemic hematoxylin formulation is disclosed that includes one or both of a stabilizer compound and an antioxidant. The disclosed composition exhibits sufficient stability to be utilized in an automated staining process. Methods of using and making the stabilized composition also are disclosed.
Efficient methods for identifying biomarkers are described. The method may include identifying a tumor area. The method may further include identifying a plurality of regions. The method may also include defining, for each region, a bounding area for the region that encompasses the region. The method may include determining, for each region of a first subset of the plurality of regions, that the region is to be ascribed to the tumor, where the bounding area is fully within the tumor area. The method may further include determining, for each region of a second subset of the plurality of regions, whether to ascribe the region to the tumor based on an intersection of the region and the tumor area. The method may also include accessing a metric characterizing a biological observation and generating a result based on the metrics. The result may be used as a biomarker.
A computer implemented method (110) for identifying at least one peak in a mass spectrometry response curve is proposed. The method comprising the following steps: a) Providing (112) at least one mass spectrometry response curve by using at least one mass spectrometry device (114); b) Evaluating (116) the mass spectrometry response curve by using at least one trained model thereby identifying a start point and an end point of at least one peak of the mass spectrometry response curve, wherein the model was trained us-ing a deep learning regression architecture (118).
A method for using a federated learning classifier in digital pathology includes distributing, by a centralized server, a global model to a plurality of client devices. The client devices further train the global model using a plurality images of a specimen and corresponding annotations to generate at least one further trained model. The client devices provide further trained models to the centralized server, which aggregates the further trained models with the global model to generate an updated global model. The updated global model is then distributed to the plurality of client devices.
The present disclosure relates machine learning techniques for segmenting non-tumor regions in specimen images to support tumor detection and analysis. Particularly, aspects of the present disclosure are directed to accessing one or more images that comprise a non-target region (e.g., a non-tumor region) and a target region (e.g., a tumor region), predicting, by a two-dimensional segmentation model, segmentation maps for the non-target region based on discriminative features encoded from the one or more images, a segmentation mask for the one or more images based on the segmentation maps, applying the segmentation mask to the one or more images to generate non-target region masked images that exclude the non-target region from the one or more images, and classifying, by an image analysis model, a biological material or structure within the target region based on a set of features extracted from the non-target region masked images.
Techniques relate to object classifications using bootstrapping of region-level annotations. For each of multiple images, regions within the image can be identified. For each region, a region-specific label can be identified, a set of objects within the region can be detected, and an object-specific label can be assigned to each object. The object-specific label can be the same as the region-specific label assigned to the region within which the object is located. A training data set can be defined to include, for each image of the multiple images, object-location data (indicating intra-image location data for the detected object) and label data (indicating the object-specific labels assigned to the objects). An image-processing model can be trained using the training data. Training can include learning values for a set of parameters that define calculations performed by the image-processing model.
The present disclosure relates to techniques for segmenting and detecting cells within image data using transfer learning and a multi-task scheduler. Particularly, aspects of the present disclosure are directed to accessing a plurality of images of one or more cells, extracting three labels from the plurality of images, where the three labels are extracted using a Voronoi transformation, a local clustering, and application of repel code, training, by a multi-task scheduler, a convolutional neural network model based on three loss functions corresponding to the three labels, generating, by the convolutional neural network model, a nuclei probability map and a background probability map for each of the plurality of images based on the training with the three loss functions, and providing the nuclei probability map and the background probability map.
The present disclosure relates to automated systems and methods for quantitatively determining an unmasking status of a biological specimen subjected to an unmasking process (e.g. an antigen retrieval process and/or a target retrieval process) using a trained unmasking status estimation engine. In some embodiments, the trained unmasking status estimation engine comprises a machine learning algorithm based on a projection onto latent structure regression model. In some embodiments, the trained unmasking status estimation engine includes a neural network.
The present disclosure relates to automated systems (200) and methods for quantitatively determining a fixation duration of a biological specimen using a trained fixation estimation engine (210). In some embodiments, the trained fixation estimation (210) engine includes a neural network. In some embodiments, the trained fixation estimation (210) engine includes a supervised classifier.
G01N 21/35 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
The present disclosure relates to automated systems and methods for predicting an expression of one or more biomarkers in a sample of a biological specimen. In some embodiments, the sample is one which has an unknown fixation status, or one where the duration of fixation to which the sample was subject is unknown. In some embodiments, the predicted expression is a quantitative estimation of the percent positivity of one or more biomarkers. In other embodiments, the predicted expression is a quantitative estimation of the staining intensity of one or more biomarkers. In some embodiments, the systems and methods utilize a trained biomarker expression estimation engine which has been trained with a plurality of training samples, where the trained biomarker expression estimation engine is adapted to derive biomarker expression features from the sample. In some embodiments, the trained biomarker expression estimation engine comprises a machine learning algorithm based on a projection onto latent structure regression model. In some embodiments, the trained biomarker expression estimation engine includes a neural network.
G01N 21/35 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
The present disclosure is directed to specimen processing assemblies including (a) a lower plate (10), (b) an upper plate (30) complementary to the lower plate, and (c) a chamber formed therefrom. In some embodiments, the formed chamber is adapted to perform an unmasking operation, e.g. antigen retrieval and/or target retrieval. In some embodiments, the specimen processing assemblies are configured to maintain a specimen-bearing substrate (15) horizontal during all processing steps. The present disclosure is also directed to systems including one or more independently operable specimen processing assemblies.
Disclosed herein are systems and methods of estimating the autofluorescence (AF) signal and other non-target signals in each channel of a multi-channel image of a biological sample that is stained with one or more fluorescent labels. In some embodiments, the estimated autofluorescence signal can then be subtracted or masked from the multi-channel image. In some embodiments, the autofluorescence-removed multichannel image can then be used for further processing (e.g. image analysis, etc.).
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH (USA)
F. HOFFMANN-LA ROCHE AG (Switzerland)
Inventor
Shanmugam, Kandavel
Sinicrope, Frank A.
Abstract
Immune context scores are calculated for stage IV colorectal tumor tissue samples using non-continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a density of human CD8+ cells in a region of interest including a tumor core to generate an immune context score. The immune context score can then be used as a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.
In some embodiments, the present disclosure is directed to coatings or thin films comprising a dye or stain embedded within a matrix, e.g. a polymer matrix.
THE FRANCIS CRICK INSTITUTE LIMITED (United Kingdom)
THE ROYAL MARSDEN NHS FOUNDATION TRUST (United Kingdom)
F. HOFFMANN LA ROCHE AG (Switzerland)
Inventor
Alexander, Nelson R.
Stanislaw, Stacey
Litchfield, Kevin Richard
Turajlic, Samra
Abstract
Disclosed herein is a method of deriving a plurality of genetic variants from a homogenized input sample. Also disclosed herein are methods of identifying a plurality of genetic variants in a sample comprising: homogenizing one or more input samples to provide a homogenized sample; preparing genomic material isolated from the homogenized input sample for sequencing; and identifying the plurality of genetic variants within sequencing data derived after sequencing the prepared genomic material.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
53.
METHODS AND SYSTEMS FOR PREPARING AND ANALYZING CELLULAR SAMPLES FOR MORPHOLOGICAL CHARACTERISTICS AND BIOMARKER EXPRESSION
This disclosure relates generally to the use of automated platforms in the preparation of biomarker-stained cellular samples for microscopic analysis and use of such stained cells in the diagnosis of certain conditions. Disclosed herein is a method of affinity staining a Romanowsky-type stained sample on automated advanced staining systems, wherein the automated advanced stainer destains the sample prior to contact with a biomarker-specific reagent. Also disclosed herein are methods of processing body fluid samples for morphological and biomarker analysis by depositing cells of the sample in a thin layer onto one or more solid supports, staining at least one such solid support with a Romanowsky-type stain and staining at least one such solid support for one or more biomarkers useful for categorizing one or more cells of the sample.
Aspects of the present disclosure pertain to systems and methods for enhancing brightfield or darkfield images to better enable nucleus detection. In some embodiments, the systems and methods described herein are useful for identifying membrane stain biomarkers as well as nuclear/cytoplasm stain biomarkers in stained images of biological samples. In some embodiments, the presently disclosed systems and methods enable quick and accurate nucleus detection in stained images of biological samples, especially for original stained images of biological samples where the nuclei appear faint.
The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.
A scoring functions is developed and used for identifying patients who might be responsive to a PD-l axis directed therapy. The scoring functions are obtained by extracting features from multiplex-stained sections, selecting features that correlate with response to the therapy using a feature selection function, and fitting one or more of the selected features to a plurality of candidate scoring functions. A candidate scoring function showing the desired balance between predictive sensitivity and specificity may then selected for incorporation into a scoring system that includes at least an image analysis system.
Disclosed herein are novel coumarin-based reagents, e.g. linkers, and conjugates including one or more of the disclosed coumarin-based reagents. In some embodiments, the presence of a coumarin moiety within the coumarin-based reagents enables detection of labels which are typically difficult to detect, e.g. certain haptens.
C07D 311/16 - Benzo [b] pyrans, not hydrogenated in the carbocyclic ring with oxygen or sulfur atoms directly attached in position 2 not hydrogenated in the hetero ring substituted in position 7
C07D 405/12 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing two hetero rings linked by a chain containing hetero atoms as chain links
C07D 405/14 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing three or more hetero rings
C07D 407/12 - Heterocyclic compounds containing two or more hetero rings, at least one ring having oxygen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a chain containing hetero atoms as chain links
C07D 413/12 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and oxygen atoms as the only ring hetero atoms containing two hetero rings linked by a chain containing hetero atoms as chain links
C07D 413/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and oxygen atoms as the only ring hetero atoms containing three or more hetero rings
C07D 417/12 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and sulfur atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a chain containing hetero atoms as chain links
C07D 417/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and sulfur atoms as the only ring hetero atoms, not provided for by group containing three or more hetero rings
Materials and methods for detecting NTRK rearrangements via affinity staining. Samples are stained with a biomarker-specific reagent (such as an antibody) that binds to a retained portion of TrkA, TrkB, and/or TrkC. The staining pattern is evaluated, and the presence of a Trk fusion is determined by detecting whether or not the sample has at least a threshold number of cells having a threshold staining intensity. In some cases, the same scoring methodology is applied regardless of the staining localization pattern. In other cases, a cytoplasmic and/or membranous localization is scored by a first methodology, whereas a nuclear localization is scored by a second methodology. The methods disclosed herein may be applied across non-endocrine solid tumor types.
The present disclosure is directed to digital pathology systems and server systems, each of which may comprise caches pre-populated with biological image data, including biological image data which was been pre-processed into a destination file format. Caches can be populated with biological image data on the digital pathology system and/or server system for retrieval of biological image data, facilitating real-time or near real-time visualization of biological image data in a browser or on the digital pathology system. Biological image data on a server can be automatically pre-processed as scanned image data is received.
A method, system, and computer program product for an image visualization system (120) that includes a contextually adaptive digital pathology interface. At least one image of a biological sample stained for the presence of one or more biomarkers is obtained (300). The image is displayed on a display screen at a first zoom level (310), in which a first subset of user selectable elements are contemporaneously displayed (320). As a result of user input, the image being is displayed at a second zoom level (330), in which a second subset of user selectable elements are contemporaneously displayed with the image (340). The one or more elements within the second subset of user selectable elements are disabled or hidden at the first zoom level, or one or more elements within the first subset of user selectable elements are disabled or hidden at the second zoom level.
Methods and compositions for accurate identification of Parkinson's disease are disclosed. More particularly, the disclosure is directed to the determination of Parkinson's disease in ante-mortem tissue samples.
A61P 25/28 - Drugs for disorders of the nervous system for treating neurodegenerative disorders of the central nervous system, e.g. nootropic agents, cognition enhancers, drugs for treating Alzheimer's disease or other forms of dementia
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
63.
SYSTEMS FOR AUTOMATED IN SITU HYBRIDIZATION ANALYSIS
The present disclosure provides for image processing systems and methods for automatically analyzing digital images of biological samples stained for the presence of protein and/or nucleic acid biomarkers (311) and automatically detecting and quantifying signals corresponding to one or more biomarkers (314). The present disclosure also provides systems and methods for the clinical interpretation of dual ISH slides where the cells to score are automatically selected (e.g. by using one or more cell detection and identification algorithms (204)). By automatically detecting, identifying, and selecting cells for assessment, it is believed that subjectivity is reduced or eliminated. The automated systems and methods also allow for an increased number of cells to be considered for scoring as compared with manual dot counting methods, thereby increasing detection sensitivity, ultimately enabling improved patient care and treatment.
Systems and methods that enable automated processing of specimens carried on microscope slides are described herein. Aspects of the technology are directed, for example, to automated specimen processing systems configured to use micro fluidic slide processing modules to robotically process tissue specimens. The slide processing modules can include reagents and a flow cell with a reaction chamber for holding the tissue specimens and reagent.
Methods for histochemical and cytochemical detection of oncogenic rearrangements of genes that result in expression of a fusion protein; materials, kits, and systems useful in such methods; and products resulting from performance of such methods are disclosed herein. At least two protein binding entities are provided: one targeting a portion of a wild-type protein that is retained in a fusion protein and a one targeting a portion of the wild type protein that is lost during the rearrangement that forms the fusion protein. A sample of a tissue suspected of harboring the fusion protein is stained with each of the two entities (either in simplex format or multiplex format), and the staining pattern resulting from binding of the entities is compared to determine the presence or absence of the fusion protein.
Systems and methods that enable automated processing of specimens carried on microscope slides are described herein. Aspects of the technology are directed, for example, to an automated staining system that includes an instrument including an end effector and a stainer unit-receiving station that receives a stainer unit such that the end effector delivers reagent from the reagent reservoirs to a reaction chamber of the slide stainer unit. The stainer unit can carry fresh reagents, waste material, and components used to handle the reagents.
Systems and methods that enable automated processing of specimens carried on microscope slides are described herein. Aspects of the technology are directed, for example, to an automated staining system including a dispensing instrument that receives a processing module to deliver reagent to a reaction chamber of the slide processing module. The processing module can carry fresh reagents, waste material, and the side.
The invention is a method of identifying a cognate antigen for a T-cell receptor using neoantigens from a patient's tumor cells combined with the patient's T-cells and using cell sorting, genome sequencing, expressing TCR genes, presenting tumor neoantigens on MHC complex and uniquely barcoding the T-cells where TCR recognition occurs to tag all components of the TCR recognition complex.
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
G01N 33/569 - Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
69.
UNIVERSAL OR NORMALIZED ANTIBODY FRAMEWORKS FOR IMPROVED FUNCTIONALITY AND MANUFACTURABILITY
The invention provides methods of designing and manufacturing universal or normalized sequence templates for rabbit monoclonal antibodies for diagnostic applications. The invention further provides for methods of optimizing desirable antibody properties, such as thermal stability, long-term stability, expression, deamination/oxidation, and/or aggregation. The invention further provides for universal or normalized rabbit monoclonal antibody templates or frameworks and antibodies derived therefrom.
A method for predicting responsiveness to a HER2-directed therapy by assessing HER2 heterogeneity in a tumor includes contacting a sample of the tumor with a biomarker- specific reagent that specifically binds to HER2 protein and detecting HER2 protein in the sample, contacting the sample of the tumor with a first nucleic acid probe that specifically binds HER2 genomic DNA and detecting HER2 gene amplification status in the sample, contacting the sample of the tumor with a second nucleic acid probe that specifically binds HER2 RNA and detecting HER2 RNA status in the sample scoring the HER2 protein (IHC), HER2 gene (DISH), and HER2 RNA (RNA-ISH), predicting that the tumor is responsive to the HER2-directed therapy if the tumor reveals a first foci having a first score and a second score, in which the first score and the second score are not the same.
The present disclosure provides for systems and methods for detecting and estimating signals corresponding to one or more bio markers in biological samples stained for the presence of protein and/or nucleic acid biomarkers.
The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects including lymphocytes. In some embodiments, a shape metric is derived for each detected and segmented lymphocyte and the shape metric is stored along with other relevant data.
Disclosed herein is a method of acquiring a high-resolution scan of a biological sample disposed on a substrate with a scanning device, the method comprising: receiving, on a graphical user interface, a first user input corresponding to user configurable scanning settings; receiving, on a graphical user interface, a second user input to initiate scanning based on the received series of user inputs corresponding to user configurable scanning settings; and displaying, on the graphical user interface, a visualization of one or more placeholders populated with one or more of scanning operation status information, image data, and at least a portion of the user configurable scanning settings.
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH (USA)
F. HOFFMANN-LA ROCHE AG (Switzerland)
Inventor
Shanmugam, Kandavel
Sinicrope, Frank A.
Abstract
Immune context scores are calculated for stage III colorectal tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a density of human CD3+ cells in a region of interest including an invasive margin. A continuous scoring function is then applied to a feature vector, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression- free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.
An automated specimen processing system is provided for performing slide processing operations on slides bearing biological samples. In some embodiments, the disclosed specimen processing system includes a barcode reader having a heated window. In some embodiments, the barcode reader having the heated window is configured to read information stored within a label affixed to a slide, whereby the barcode reader may be operated within a hot and/or humid environment. A method for automated processing of slides also is provided, whereby the method utilizes the information retrieved from a label affixed to determine which one or more slide processing operations to perform.
G06K 7/10 - Methods or arrangements for sensing record carriers by corpuscular radiation
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
G01N 35/00 - Automatic analysis not limited to methods or materials provided for in any single one of groups ; Handling materials therefor
nn, wherein n is an integer ranging from 1 to 12, and where the Oligomer includes, in some embodiments, a PNA sequence having at least one substituent at a gamma carbon position. In some embodiments, the substituent at the gamma carbon position, e.g. an amino acid, a peptide, a miniPEG, or a polymer, includes at least one reporter moiety.
G01N 33/532 - Production of labelled immunochemicals
G01N 33/58 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additives; Targeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
77.
SYSTEM AND METHOD FOR CLASSIFYING CELLS IN TISSUE IMAGES BASED ON MEMBRANE FEATURES
An image analysis system and method classify cells in a tissue image. The system and method may extract at least one image feature characterizing an object in the tissue image. Based on the extracted image feature, the system and method may classify the cells according to at least one predefined membrane pattern. For each classified cell the system and method may output a class label that identifies a class to which the classified cell belongs.
Presented herein are methods of improving the consistency of staining with a counterstain. In some embodiments, the method makes use of an automated specimen processing apparatus or other staining device. In some embodiments, the staining methods are applied manually. In some embodiments, the counterstain includes eosin.
An image analysis system and method to generate selective stain segmentation images for at least one cell type of interest within a stained tissue image. The system and method may detect cells in the tissue image and generate a corresponding membrane mask image. They may classify the cells detected in the tissue image and generate a classified cells image of the cells. The system and method may further generate selective stain segmentation images for the at least one cell type of interest based on the membrane mask image and the classified cells image of the cells.
The present disclosure relates to acid fast staining compositions which are free from phenol. The present disclosure also related to a method of detecting an acid fast organism in a biological sample comprising: (a) applying an acid fast staining composition to the biological sample, the acid fast staining solution comprising a fuchsin, a base, a surfactant, and an alcohol, and wherein the acid fast staining composition is free from phenol; and (b) incubating the biological sample with the acid fast staining composition for a predetermined amount of time at a predetermined temperature.
C12Q 1/04 - Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
82.
METHOD OF COMPUTING TUMOR SPATIAL AND INTER-MARKER HETEROGENEITY
The present disclosure relates, among other things, to automated systems and methods for determining the variability between derived expression scores for a series of biomarkers between different identified cell clusters in a whole slide image. In some embodiments, the variability between derived expression scores may be a derived inter-marker heterogeneity metric.
The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.
The present disclosure relates to automated systems and methods for training a multilayer neural network to jointly and simultaneously classify cells and regions from a set of training images. The present disclosure also relates to automated systems and methods for using a trained multilayer neural network to classify cells within an unlabeled image.
Disclosed is a device for contactlessly mixing fluid present on the upper surface of the slide, where the device comprises a first nozzle array and a second nozzle array, the first nozzle array adapted to impart a bulk fluid flow to the fluid present on the upper surface of the slide, and the second nozzle array adapted to impart at least a first regional fluid flow to at least a portion of the fluid present on the upper surface of the slide
A tissue analysis system and method for the spectral deconvolution of a RGB digital image obtained from a stained biological tissue sample, by estimating the stain component images that are obtained from a staining system configuration, where the reference stain vectors are assumed to be sampled from a known color distribution. The prior knowledge of stain variability of the staining system is adopted as initial reference stain vectors and statistical distribution of their variability. Based on the initial reference stain vectors distribution, the tissue analysis system determines both the reference stain vectors and stain component images of the input image. The image is then deconvoluted based on the reference stain vectors and stain component images.
A system and a method for unveiling poorly visible or lightly colored nuclei in an input image are disclosed. An input image is fed to a color deconvolution module for deconvolution into two color channels that are processed separately before being combined. The input image is deconvolved into two separate images: a stain image and a counter stain image. A complement of the stain image is generated in order to clearly reflect the locations of the poorly visible or light-colored nuclei. The complement image and the counter stain image are optionally normalized and then combined and segmented, to generate an output image with clearly defined nuclei. Alternatively, the complement of the stain image and the counter stain image can optionally be normalized, and then segmented prior to being combined to generate the output image.
The present disclosure provides systems and methods for separating colors in an image by automatically selecting color reference vectors that take into consideration the effect of light scattering, and principally how the light scattering changes the proportions of RGB channel signals in detected light at varying stain concentrations.
Disclosed herein are systems and methods for normalizing the titer of a first stain to a titer of the same stain in a template image. Also disclosed are methods of assessing stain titer levels.
Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G01N 33/574 - Immunoassay; Biospecific binding assay; Materials therefor for cancer
Disclosed herein are systems and methods of calibrating a microscope or an imaging system prior to acquiring image data of a sample. In some embodiments, a method is disclosed including the steps of (a) running a power output calibration module (603) to calibrate an imaging apparatus for repeatability (30); (b) running an image intensity calibration module (604) to calibrate the imaging apparatus for reproducibility and to mitigate differences in detection efficiency between channels (31); (c) collecting image data from a microscope or imaging system (32); (d) optionally running an unmixing module (607) to unmix the collected image data into individual image channel images (33); and (e) optionally running a contrast agent intensity correction module (605) to calibrate for differences in brightness between different contrast agents (34).
G01N 21/27 - Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection
In one aspect of the present disclosure is a method of contactlessly urging, directly, or moving a substance on the surface of a substrate, the method employing a gas knife (300) configured to produce a gas curtain having parallelogram flow.
A microscope slide holder (5) comprising a slide support member (20) and at least one acoustic source (30) for introducing acoustic waves to a microscope slide (10) in communication with the slide support member (20) such that one or more fluids present on the surface of the microscope slide (10) are contactlessly mixed.
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 disclosure provides a method of separating cellular particles from a tissue sample and then sorting the cellular particles into two or more cellular particle populations.
The disclosure provides a method of stitching tile images comprising performing a local registration to determine spatial relationships between pairs of adjacent tile images, the spatial relationships determined using normalized cross correlation (NCC) scores computed within a multi-resolution framework; and performing a global placement to position all scanned tile images relative to one another, wherein the global placement is determined with weighted least squares utilizing the determined spatial relationships between all adjacent greyscale tile images and the NCC scores as weights.
A method of preparing a stage for use in a slide imaging apparatus including positioning a stage in relation to a flat surface so that the flat surface is positioned in front of the top surfaces of slide support pin bases on the top surface of the stage. The method also includes injecting a fluid pin surfacing material configured to solidify into the hole of each slide support pin base so that at least some of the fluid pin surfacing material exits the hole at the top surface of the slide support pin bases and pushes up against the flat surface. The method also includes removing the flat surface so that a tip of solid pin surfacing material is formed on the top surface of each slide support pin base, thereby providing the stage with a plurality of slide support pins.
A slide imaging apparatus that includes a copy holder moving system and an imaging system. The copy holder moving system includes a movable stage configured to move along first and second slide movement axes relative to the imaging system, wherein the imaging system is configured to form an image of a sample mounted on a slide located in the/each imaging location on the movable stage during an image forming process that includes the movable stage moving relative to the imaging system along the first and second slide movement axes. The copy holder moving system also includes a copy holder configured to be mounted to the movable stage, wherein the copy holder is configured to be mounted to the movable stage in each of a plurality of indexing positions.
Systems and methods disclosed herein describe a platform that automatically creates and executes a scoring guide for use in anatomical pathology. The platform can employ a fully-automated workflow for clustering the biological objects of interest and for providing cell-by-cell read-outs of heterogeneous tumor biomarkers based on their stain appearance. The platform can include a module for automatically creating and storing a scoring guide in a training database based on training digital images (240, 250), and an object classification module that executes the scoring guide when presented with new digital images to be scored pursuant to the scoring guide (299).
Methods and systems are provided for quantitative immunohistochemistry (IHC) of a target protein molecule including a secreted target protein molecule. The method comprises introducing to the sample: a primary antibody specific for the target protein molecule; a secondary antibody conjugated to a secondary antibody enzyme, the secondary antibody is specific for the primary antibody; a tyramide conjugated with a tyramide hapten, wherein the secondary antibody enzyme catalyzes deposition of the tyramide hapten onto the sample; a tertiary antibody conjugated with a tertiary antibody enzyme, the tertiary antibody is specific for the tyramide hapten; and a chromogen, wherein the tertiary antibody enzyme catalyzes a reaction with the chromogen to make the chromogen visible. The chromogen is visible as a punctate dot using microscopy.
G01N 33/542 - Immunoassay; Biospecific binding assay; Materials therefor with immune complex formed in liquid phase with steric inhibition or signal modification, e.g. fluorescent quenching
G01N 33/543 - Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
G01N 33/58 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
C12Q 1/28 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving oxidoreductase involving peroxidase
patents
