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 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
3.
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.
The present disclosure is directed to a computer system designed to (i) receive a series of images as input; (ii) compute a number of metrics derived from focus features and color separation features within the images; and (iii) evaluate the metrics to return (a) an identification of the most suitable z-layer in a z-stack, given a series of z-layer images in a z-stack; and/or (b) an identification of those image tiles that are more suitable for cellular based scoring by a medical professional, given a series of image tiles from an area of interest of a whole slide scan.
A method includes accessing a digital pathology image that depicts tumor cells sampled from a subject. A plurality of patches may be selected from the digital pathology image, wherein each of the patches depicts tumor cells. A mutation prediction may be generated for each of the patches, wherein the mutation prediction represents a prediction of a likelihood that an actionable mutation appears in the patch. Based on the plurality of mutation predictions, a prognostic prediction related to one or more treatment regimens for the subject may be generated. The prognostic prediction may be based on determining one or more mutational contexts of the digital pathology image as an unknown driver or a tumor suppressor, an oncogene driver mutation, or a gene fusion.
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
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.
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
7.
SINGLE-STRANDED OLIGONUCLEOTIDE PROBES FOR CHROMOSOME OR GENE COPY ENUMERATION
Single-stranded oligonucleotide probes, systems, kits and methods for chromosome enumeration, gene copy enumeration, or tissue diagnostics. The probes are particularly suited for detecting gene amplification, deletion, or rearrangement in tissue samples in a single, dual, or multiplexed assay. The probes exhibit improved performance compared to industry leading dual-stranded probes; particularly in terms of the rate of hybridization.
C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
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 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
9.
SIZE-BASED SEPARATION OF DISSOCIATED FIXED TISSUES
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.
B01L 3/00 - Récipients ou ustensiles pour laboratoires, p.ex. verrerie de laboratoire; Compte-gouttes
C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
G01N 1/28 - Préparation d'échantillons pour l'analyse
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
10.
AUTOMATED SEGMENTATION OF ARTIFACTS IN HISTOPATHOLOGY IMAGES
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 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 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.
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.
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.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
Methods for in situ detecting proximity of two targets of interest featuring an antibody conjugated with a cleavable bridge component having a detectable moiety and an antibody conjugated with a non-cleavable bridge component. The bridge components each have a chemical ligation group adapted to form a covalent bond under particular conditions and when the targets are in close proximity. Following covalent bond formation, the cleavable bridge component can be cleaved from the antibody, effectively transferring the detectable moiety to the non-cleavable bridge component. Detection of the detectable moiety is indicative of the targets being in close proximity. The methods are compatible with both chromogenic and fluorogenic detection systems. The methods may be used to perform assays wherein one or more than one proximity event is detected on the same slide.
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/542 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet avec formation d'un complexe immunologique en phase liquide avec inhibition stérique ou modification du signal, p.ex. extinction de fluorescence
G01N 33/543 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet avec un support insoluble pour l'immobilisation de composés immunochimiques
15.
OPTIMIZED DATA PROCESSING FOR MEDICAL IMAGE ANALYSIS
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.
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 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
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.
C12Q 1/6804 - Analyse d’acides nucléiques utilisant des immunogènes
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
18.
TRANSFORMATION OF HISTOCHEMICALLY STAINED IMAGES INTO SYNTHETIC IMMUNOHISTOCHEMISTRY (IHC) IMAGES
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.
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.
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
20.
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 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
21.
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.
A microscope scanner is provided comprising a detector array for obtaining an image from a sample and a sample holder configured to move relative to the detector array. The sample holder can be configured to move to a plurality of target positions relative to the detector array in accordance with position control signals issued by a controller and the detector array is configured to capture images during an imaging scan based on the position control signals.
An automated system is provided for performing slide processing operations on slides bearing biological samples. In one embodiment, the disclosed system includes a slide tray holding a plurality of slides in a substantially horizontal position and a workstation that receives the slide tray. In a particular embodiment, a workstation delivers a reagent to slide surfaces without substantial transfer of reagent (and reagent borne contaminants such as dislodged cells) from one slide to another. A method for automated processing of slides also is provided.
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
B01L 9/00 - Dispositifs de support; Dispositifs de serrage
The invention provides anti-human pro-epiregulin and anti-human amphiregulin antibodies and methods of using the same. Anti-EREG antibodies raised against amino acids 148-169 and 156-169 of the human EREG protein, and anti-AREG antibodies raised against amino acids 238-252 of the human AREG protein are disclosed. Methods of using these antibodies to detect EREG and AREG and kits and other products for performing such methods are also disclosed.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
C07K 16/22 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des facteurs de croissance
C07K 16/30 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des récepteurs, des antigènes de surface cellulaire ou des déterminants de surface cellulaire provenant de cellules de tumeurs
Disclosed herein are novel quinone methide analog precursors and embodiments of a method and a kit of using the same for detecting one or more targets in a biological sample. The method of detection comprises contacting the sample with a detection probe, then contacting the sample with a labeling conjugate that comprises an enzyme. The enzyme interacts with a quinone methide analog precursor comprising a detectable label, forming a reactive quinone methide analog, which binds to the biological sample proximally to or directly on the target. The detectable label is then detected. In some embodiments, multiple targets can be detected by multiple quinone methide analog precursors interacting with different enzymes without the need for an enzyme deactivation step.
C07F 9/6561 - Composés hétérocycliques, p.ex. contenant du phosphore comme hétéro-atome du cycle contenant des systèmes de plusieurs hétérocycles déterminants condensés entre eux ou condensés avec un carbocycle ou un système carbocyclique commun, avec ou sans autres hétérocycles non condensés
C07F 9/6558 - Composés hétérocycliques, p.ex. contenant du phosphore comme hétéro-atome du cycle contenant au moins deux hétérocycles différents ou différemment substitués ni condensés entre eux ni condensés avec un carbocycle commun ou un système carbocyclique commun
C07F 9/12 - Esters des acides phosphoriques avec des composés hydroxyarylés
C07D 209/14 - Radicaux substitués par des atomes d'azote ne faisant pas partie d'un radical nitro
C07H 15/203 - Carbocycles monocycliques autres que des cycles cyclohexane; Systèmes carbocycliques bicycliques
C07H 15/26 - Radicaux acycliques ou carbocycliques substitués par des hétérocycles
C12Q 1/42 - Procédés de mesure ou de test faisant intervenir des enzymes, des acides nucléiques ou des micro-organismes; Compositions à cet effet; Procédés pour préparer ces compositions faisant intervenir une hydrolase une phosphatase
C07H 15/207 - Cycles cyclohexane non substitués par des atomes d'azote, p.ex. kasugamycines
G01N 33/58 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des substances marquées
C07D 403/06 - Composés hétérocycliques contenant plusieurs hétérocycles, comportant des atomes d'azote comme uniques hétéro-atomes du cycle, non prévus par le groupe contenant deux hétérocycles liés par une chaîne carbonée ne contenant que des atomes de carbone aliphatiques
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.
C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images. The basis for alignment may be a line-based registration process, wherein sets of lines are computed on the boundary regions computed for the two images, where the boundary regions are obtained using information from two domains—soft-weighted foreground images and gradient magnitude images. The binary mask image, based on whose boundary the line features are computed, may be generated by combining two binary masks—a first binary mask is obtained on thresholding a soft-weighted (continuous valued) foreground image, which is computed based on the stain content in an image, while a second binary mask is obtained after thresholding a gradient magnitude domain image, where the gradient is computed from the grayscale image obtained from the color image.
G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images utilisant des procédés basés sur les caractéristiques
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
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.
C07J 19/00 - Stéroïdes normaux contenant du carbone, de l'hydrogène, un halogène ou de l'oxygène, substitués en position 17 par un cycle lactonique
G01N 33/58 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des substances marquées
A61K 47/68 - Préparations médicinales caractérisées par les ingrédients non actifs utilisés, p.ex. les supports ou les additifs inertes; Agents de ciblage ou de modification chimiquement liés à l’ingrédient actif l’ingrédient non actif étant chimiquement lié à l’ingrédient actif, p.ex. conjugués polymère-médicament l’ingrédient non actif étant un agent de modification l’agent de modification étant un anticorps, une immunoglobuline ou son fragment, p.ex. un fragment Fc
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 configured to produce a gas curtain having parallelogram flow.
F26B 21/00 - Dispositions pour l'alimentation ou le réglage de l'air ou des gaz pour le séchage d'un matériau solide ou d'objets
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
30.
METHODS AND SYSTEMS FOR EVALUATION OF IMMUNE CELL INFILTRATE IN TUMOR SAMPLES
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.
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 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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 is 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.
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p.ex. glissement, rotation, agrandissement ou changement de couleur
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 to calibrate an imaging apparatus for repeatability; (b) running an image intensity calibration module to calibrate the imaging apparatus for reproducibility and to mitigate differences in detection efficiency between channels; (c) collecting image data from a microscope or imaging system; (d) optionally running an unmixing module to unmix the collected image data into individual image channel images; and (e) optionally running a contrast agent intensity correction module to calibrate for differences in brightness between different contrast agents.
G01N 21/27 - Couleur; Propriétés spectrales, c. à d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en utilisant la détection photo-électrique
Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
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 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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.
G02B 7/38 - Systèmes pour la génération automatique de signaux de mise au point utilisant des techniques liées à la netteté de l'image mesurée en différents points de l'axe optique
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.
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
39.
AUTOMATIC ASSAY ASSESSMENT AND NORMALIZATION FOR IMAGE PROCESSING
Disclosed herein are systems and methods for of assessing stain titer levels. An exemplary method includes generating a set of field of views for the image or the region of the image, selecting field of views from the set of field of views that meet predefined criteria, creating a series of patches within each of the selected field of views, retaining patches from the series of patches that meet predefined criteria indicative of a presence of the stain for which the titer is to be estimated, deriving stain color features and stain intensity features pertaining to the stain from the retained patches, estimating a titer score for each of the retained patches based on the stain color features and the stain intensity features, and calculating a weighted average score for the titer of the stain based on the estimated titer score for each of the retained patches.
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G16H 70/60 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pathologies
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G06F 16/535 - Filtrage basé sur des données supplémentaires, p.ex. sur des profils d'utilisateurs ou de groupes
G01N 15/14 - Recherche par des moyens électro-optiques
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH (USA)
Inventeur(s)
Ahanonu, Eze, O.
Mcmahon, Sara, M.
Shanmugam, Kandavel
Sinicrope, Frank, A.
Williams, Crystal, Sue
Yan, Dongyao
Zhang, Wenjun
Abrégé
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).
The present disclosure is directed to opposables including a body having a plurality of cavities disposed therein. Each cavity can be designed to contain one or more reagents, liquids, or fluids which may be applied to a specimen-bearing surface. In some embodiments, the cavities include one or more reagent chambers, the reagent chambers can have one or more seals such that the reagents, liquids, or fluids contained therein may be stored and released to the specimen-bearing surface.
G01N 35/10 - Dispositifs pour transférer les échantillons vers, dans ou à partir de l'appareil d'analyse, p.ex. dispositifs d'aspiration, dispositifs d'injection
G02B 21/34 - Lames de microscope, p.ex. montage d'échantillons sur des lames de microscope
B01L 7/00 - Appareils de chauffage ou de refroidissement; Dispositifs d'isolation thermique
B05C 11/02 - Appareils pour étaler ou répartir des liquides ou d'autres matériaux fluides déjà appliqués sur une surface; Réglage de l'épaisseur du revêtement
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
42.
HYBRID AND ACCELERATED GROUND-TRUTH GENERATION FOR DUPLEX ARRAYS
Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
43.
MULTICLASS INTERACTIVE SEGMENTATION GRAPHICAL USER INTERFACE
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.
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 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des récepteurs, des antigènes de surface cellulaire ou des déterminants de surface cellulaire
A61K 31/4745 - Quinoléines; Isoquinoléines condensées en ortho ou en péri avec des systèmes hétérocycliques condensées avec des systèmes cycliques ayant l'azote comme hétéro-atome d'un cycle, p.ex. phénanthrolines
45.
Methods, Systems, and Apparatuses for Quantitative Analysis of Heterogeneous Biomarker Distribution
Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.
A system and method for treatment of biological samples is disclosed. In some embodiments, an automated biological sample staining system (100), comprising at least one microfluidic reagent applicator (118); at least one bulk fluid applicator (116); at least one fluid aspirator; at least one sample substrate holder; at least one relative motion system; and a control system (102) that is programmed to execute at least one staining protocol on a sample mounted on a substrate that is held in the at least one sample substrate holder.
G01N 35/10 - Dispositifs pour transférer les échantillons vers, dans ou à partir de l'appareil d'analyse, p.ex. dispositifs d'aspiration, dispositifs d'injection
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.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
In one aspect of the present disclosure is a targeted sequencing workflow where an input sample comprising a sufficient quantity of genomic material is provided such minimal or no amplification cycles are utilized prior to sequencing.
The subject disclosure presents systems and computer-implemented methods for evaluating a tissue sample that has been removed from a subject. A change in speed of the energy traveling through the sample is evaluated to monitor changes in the biological sample during processing. The rate of change in the speed of the energy is correlated with the extent of diffusion. A system for performing the method can include a transmitter that outputs the energy and a receiver configured to detect the transmitted energy. A time-of-flight of acoustic waves and rate of change thereof is monitored to determine an optimal time for soaking the tissue sample in a fixative.
G01N 13/00 - Recherche des effets de surface ou de couche limite, p.ex. pouvoir mouillant; Recherche des effets de diffusion; Analyse des matériaux en déterminant les effets superficiels, limites ou de diffusion
G01N 29/07 - Analyse de solides en mesurant la vitesse de propagation ou le temps de propagation des ondes acoustiques
G01N 29/024 - Analyse de fluides en mesurant la vitesse de propagation ou le temps de propagation des ondes acoustiques
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.
A01N 59/00 - Biocides, produits repoussant ou attirant les animaux nuisibles, ou régulateurs de croissance des végétaux, contenant des éléments ou des composés inorganiques
B41J 2/01 - Machines à écrire ou mécanismes d'impression sélective caractérisés par le procédé d'impression ou de marquage pour lequel ils sont conçus caractérisés par la mise en contact sélective d'un liquide ou de particules avec un matériau d'impression à jet d'encre
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.
A method is disclosed that permits calculation of reagent concentrations (in SI units) over time and space within a tissue sample as the sample is immersed in the reagent and the reagent diffuses into the tissue sample. The disclosed method has yielded the surprising result that once a formaldehyde concentration at all points within a tissue sample exceeds about 90 mM during a cold step of a cold+hot fixation protocol, the hot step of the fixation protocol can be commenced to provide reliable detection of molecular targets and preservation of tissue morphology in downstream analyses.
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/74 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des hormones
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
A method and system are described for processing tissues according to particular processing protocols that are established based on time-of-flight measurements as a processing fluid is diffused into a tissue sample. In one embodiment, measurement of the time it takes about 70% ethanol to diffuse into a tissue sample is used to predict the time it will take to diffuse other processing fluids into the same or similar tissue samples. Advantageously, the disclosed method and system can reduce overall processing times and help ensure that only samples that require similar processing conditions are batched together.
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.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
An aldehyde fixative solution at a first temperature is caused to contact a tissue sample for a first time period, additionally an aldehyde fixative solution is caused to contact the tissue sample at a second temperature higher than the first temperature for a second time period. The first time period typically ranges from about 15 minutes up to about 4 hours, and the first temperature typically is from greater than 0° C. to at least 15° C. The second temperature typically is from greater than about 22° C. to about 55° C., and the second time period ranges from about 1 hour to about 4 hours. Using this process, improved tissue morphology and IHC staining as well as superior preservation of post-translation modification signals have been accomplished in approximately 4 hours compared to 24 hours for room temperature protocols, and more even morphology and antigen preservation are observed.
Automated system configured to perform and methods for performing one or more slide processing operations on slides bearing biological samples. The system and methods enable high sample throughput while also minimizing or limiting the potential for cross-contamination of slides. The automated systems can include features that facilitate consistency, controllability of processing time, and/or processing temperature.
G01N 35/10 - Dispositifs pour transférer les échantillons vers, dans ou à partir de l'appareil d'analyse, p.ex. dispositifs d'aspiration, dispositifs d'injection
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
58.
HISTOLOGICAL STAIN PATTERN AND ARTIFACTS CLASSIFICATION USING FEW-SHOT LEARNING
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.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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 403/06 - Composés hétérocycliques contenant plusieurs hétérocycles, comportant des atomes d'azote comme uniques hétéro-atomes du cycle, non prévus par le groupe contenant deux hétérocycles liés par une chaîne carbonée ne contenant que des atomes de carbone aliphatiques
C07F 9/6547 - Cycles à six chaînons condensés avec des carbocycles ou des systèmes carbocycliques
C07F 9/6561 - Composés hétérocycliques, p.ex. contenant du phosphore comme hétéro-atome du cycle contenant des systèmes de plusieurs hétérocycles déterminants condensés entre eux ou condensés avec un carbocycle ou un système carbocyclique commun, avec ou sans autres hétérocycles non condensés
C07F 9/655 - Composés hétérocycliques, p.ex. contenant du phosphore comme hétéro-atome du cycle comportant des atomes d'oxygène, avec ou sans atomes de soufre, de sélénium ou de tellure, comme uniques hétéro-atomes du cycle
C07D 401/10 - Composés hétérocycliques contenant plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle, au moins un cycle étant un cycle à six chaînons avec un unique atome d'azote contenant deux hétérocycles liés par une chaîne carbonée contenant des cycles aromatiques
60.
SYNTHESIS SINGLEPLEX FROM MULTIPLEX BRIGHTFIELD IMAGING USING GENERATIVE ADVERSARIAL NETWORK
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.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Disclosed herein are methods for identifying a subject as having NSCLC that is predicted or is likely to respond to treatment with an ALK inhibitor, for example crizotinib. The methods include identifying a sample including NSCLC tumor cells as ALK-positive or ALK-negative using immunohistochemistry (IHC) and scoring methods disclosed herein. A subject is identified as having NSCLC likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-positive and is identified as having NSCLC not likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-negative. According to certain embodiments of the methods, subjects predicted to respond to an ALK inhibitor may then be treated with an ALK inhibitor such as crizotinib.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
A61K 31/4545 - Pipéridines non condensées, p.ex. pipérocaïne contenant d'autres systèmes hétérocycliques contenant un cycle à six chaînons avec l'azote comme hétéro-atome du cycle, p.ex. pipampérone, anabasine
62.
SYNTHESIS SINGLEPLEX FROM MULTIPLEX BRIGHTFIELD IMAGING USING GENERATIVE ADVERSARIAL NETWORK
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.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
63.
PROCESSING SYSTEM FOR PROCESSING SPECIMENS USING ACOUSTIC ENERGY
A method for fixing a biological sample includes delivering energy through a biological sample that has been removed from a subject, while fixing the biological sample. A change in speed of the energy traveling through the biological sample is evaluated to monitor the progress of the fixation. A system for performing the method can include a transmitter that outputs the energy and a receiver configured to detect the transmitted energy. A computing device can evaluate the speed of the energy based on signals from the receiver.
Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
65.
MACHINE LEARNING MODELS FOR CELL LOCALIZATION AND CLASSIFICATION LEARNED USING REPEL CODING
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.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
66.
Method of storing and retrieving digital pathology analysis results
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.
G06K 1/00 - Méthodes ou dispositions pour marquer les supports d'enregistrement sous la forme numérique
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p.ex. de visages similaires sur les réseaux sociaux
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
67.
WHOLE-SLIDE ANNOTATION TRANSFER USING GEOMETRIC FEATURES
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.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
The present disclosure is directed to conjugates of a specific binding entity and an oligomer, i.e. [Specific Binding Entity]-[Oligomer]n, 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.
C07K 14/00 - Peptides ayant plus de 20 amino-acides; Gastrines; Somatostatines; Mélanotropines; Leurs dérivés
C07H 21/00 - Composés contenant au moins deux unités mononucléotide comportant chacune des groupes phosphate ou polyphosphate distincts liés aux radicaux saccharide des groupes nucléoside, p.ex. acides nucléiques
69.
METHOD AND SYSTEM TO DETECT SUBSTRATE PLACEMENT ACCURACY
A method and system for measuring the alignment between a substrate and a platform upon which it is disposed by using image processing algorithms are described herein. These algorithms automate the detection of edges of a microscope slide and the platform in a digital image. A reference line pattern in an image of the platform can be used to detect platform edges based on a computed location of the reference line pattern in the image.
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.
C07K 16/28 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des récepteurs, des antigènes de surface cellulaire ou des déterminants de surface cellulaire
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
The disclosure is directed to conjugates, e.g. PNA conjugates, as well as methods of employing the conjugates for detecting one or more targets in a biological sample, e.g. a tissue sample.
C12N 15/11 - Fragments d'ADN ou d'ARN; Leurs formes modifiées
C07K 2/00 - Peptides à nombre indéterminé d'amino-acides; Leurs dérivés
C07K 14/00 - Peptides ayant plus de 20 amino-acides; Gastrines; Somatostatines; Mélanotropines; Leurs dérivés
C07K 16/28 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des récepteurs, des antigènes de surface cellulaire ou des déterminants de surface cellulaire
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
G01N 33/58 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des substances marquées
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
73.
METHOD FOR PREDICTION OF THE PROGRESSION RISK OF TUMORS
The present invention concerns a method for predicting the potential for aggressive growth and/or the risk to progress to high grade cancer for tumors in cell based detection procedures. In one aspect the invention concerns the detection of overexpression of cyclin-dependent kinase inhibitor gene products as a tool for predicting the progression risk and/or potential for aggressive growth of tumors. In a second aspect the invention concerns predicting the progression risk and/or potential for aggressive growth in tumors on the basis of the simultaneous co-detection of the presence of overexpression of cyclin-dependent kinase inhibitor gene products together with the expression of markers for active cell proliferation. Further the invention concerns preparations of probes for diagnosis namely for predicting the progression risk and/or the potential for aggressive growth of tumors.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
74.
MACHINE-LEARNING TECHNIQUES FOR DETECTING ARTIFACT PIXELS IN IMAGES
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.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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.
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p.ex. glissement, rotation, agrandissement ou changement de couleur
The present disclosure is directed to methods and devices for reducing or otherwise mitigating accumulated reagent material and/or fluids within a dispense nozzle of a dispenser.
G01N 35/10 - Dispositifs pour transférer les échantillons vers, dans ou à partir de l'appareil d'analyse, p.ex. dispositifs d'aspiration, dispositifs d'injection
Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.
H04N 23/10 - Caméras ou modules de caméras comprenant des capteurs d'images électroniques; Leur commande pour générer des signaux d'image à partir de différentes longueurs d'onde
H04N 23/67 - Commande de la mise au point basée sur les signaux électroniques du capteur d'image
78.
INKJET DEPOSITION OF REAGENTS FOR HISTOLOGICAL SAMPLES
Devices and methods for the deposition of reagents onto cells or tissue samples are disclosed. Also disclosed are reagent compositions suitable for dispensing via a droplet-on-demand system.
G01N 35/10 - Dispositifs pour transférer les échantillons vers, dans ou à partir de l'appareil d'analyse, p.ex. dispositifs d'aspiration, dispositifs d'injection
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
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.
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 10/80 - Fusion, c. à d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
81.
METHODS FOR EFFICIENTLY DETERMINING DENSITY AND SPATIAL RELATIONSHIP OF MULTIPLE CELL TYPES IN REGIONS OF TISSUE
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 microscope scanner is provided comprising a detector array for obtaining an image from a sample and a sample holder configured to move relative to the detector array. The sample holder can be configured to move to a plurality of target positions relative to the detector array in accordance with position control signals issued by a controller and the detector array is configured to capture images during an imaging scan based on the position control signals.
A computer implemented method for identifying at least one peak in a mass spectrometry response curve is provided comprising: a) providing at least one mass spectrometry response curve by using at least one mass spectrometry device; b) evaluating 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 using a deep learning regression architecture.
G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
84.
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.
In general, the presently disclosed technology relates to identification of cancer subtypes. More specifically, the technology relates to methods for determining molecular drivers of cancer and/or progression using a multivariate image data and statistical analysis of in-situ molecular markers and morphological characteristics in the same cells of a biological sample suspected of b cancer. This analysis takes place after a single acquisition that obtains the molecular and anatomic morphology data in parallel. The analysis compares specific morphological and molecular markers to known samples exhibiting particular genetic drivers of the cancer. This method provides statistical information that allows for an increased confidence in the identification of specific molecular drivers of the cancer.
C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
The present disclosure describes a method of foreground segmentation and nucleus ranking for scoring dual ISH images. The method has been developed to better identify those nuclei, within a selected field of view, that meet the criteria for dual ISH scoring.
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 herein are systems and methods for of assessing stain titer levels. An exemplary method includes generating a set of field of views for the image or the region of the image, selecting field of views from the set of field of views that meet predefined criteria, creating a series of patches within each of the selected field of views, retaining patches from the series of patches that meet predefined criteria indicative of a presence of the stain for which the titer is to be estimated, deriving stain color features and stain intensity features pertaining to the stain from the retained patches, estimating a titer score for each of the retained patches based on the stain color features and the stain intensity features, and calculating a weighted average score for the titer of the stain based on the estimated titer score for each of the retained patches.
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
G16H 70/60 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pathologies
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G06F 16/535 - Filtrage basé sur des données supplémentaires, p.ex. sur des profils d'utilisateurs ou de groupes
G01N 15/14 - Recherche par des moyens électro-optiques
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.
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.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p.ex. l’édition
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.
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
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.
C07F 9/547 - Composés hétérocycliques, p.ex. contenant du phosphore comme hétéro-atome du cycle
C12Q 1/6804 - Analyse d’acides nucléiques utilisant des immunogènes
G01N 33/58 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des substances marquées
94.
OPTIMIZED DATA PROCESSING FOR MEDICAL IMAGE ANALYSIS
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.
Automated methods for extracting nucleic acid from one or more tissue samples disposed on slides are disclosed. The methods utilize an automated slide staining apparatus that dispenses a plurality of nucleic acid extraction reagents onto the tissue sample, thus creating an extracted nucleic acid sample. The extracted nucleic acid sample may be used directly in downstream applications such as nucleic acid amplification or sequencing procedures, or may be further purified.
Automated systems to make target compounds from slide stainer waste streams inactive utilizing advanced oxidation processes are described herein. Advanced oxidation processes are promoted by UV irradiation and further accelerated by use of radical initiators, such as hydrogen peroxide. The automated systems further include mechanisms for segregating components of the waste streams.
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
A microscope slide holder comprising a slide support member and at least one acoustic source for introducing acoustic waves to a microscope slide in communication with the slide support member such that one or more fluids present on the surface of the microscope slide are contactlessly mixed.
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
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
B01F 33/40 - Mélangeurs utilisant l'agitation de gaz ou de liquide, p.ex. avec des tubes d'alimentation en air
100.
PASSIVE, GRAVITY-DRIVEN SYSTEM FOR TREATMENT OF AN EFFLUENT IN A DIAGNOSTIC SYSTEM
Automated systems to make target compounds from slide stainer waste streams inactive utilizing advanced oxidation processes are described herein. Advanced oxidation processes are promoted by UV irradiation and further accelerated by use of radical initiators, such as hydrogen peroxide. The automated systems further include mechanisms for segregating components of the waste streams.
G01N 35/00 - Analyse automatique non limitée à des procédés ou à des matériaux spécifiés dans un seul des groupes ; Manipulation de matériaux à cet effet
