The present disclosure is directed to a triplex immunohistochemical assay for detecting the colocalization of the ER, PR, and Ki-67 biomarkers in cells or cell nuclei. It is believed that the brightfield triplex immunohistochemical assay of the present disclosure may serve as a prognostic assay for ER-positive breast cancer, facilitating the identification of disease-free survivors among ER-positive breast cancer patients treated with hormone therapy.
Methods of evaluating mRNA or cDNA are provided. Instead of evaluating the mRNA or cDNA as a whole, multiple distinct portions of the mRNA or cDNA (such as different exons or untranslated regions) are separately quantified. The separate quantities are then evaluated by a trained classifier, which converts the separate quantities to a composite score indicative of the expression level of the mRNA or corresponding cDNA.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 25/00 - ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
Embodiments described herein pertain to systems and methods for imaging. An imaging system may include a castellated optical element, a CMOS image sensor, and color filtering elements. The CMOS image sensor may include focus areas, a line-scan area, a 2D imaging area, and look-ahead gaps. The imaging system may be configured scan and capture bi-directionally, forward-focused brightfield and fluorescence images of one or more slides comprising at least one biological material.
A slide carrier includes: a base support; and a slide platform having a surface that is parallel to a first plane defined by a first vector and a second vector, wherein a vector extending in a direction opposite to the direction of gravity is normal with respect to a second plane defined by a third vector and a fourth vector, an angle between the first vector and the third vector is greater than zero degrees and less than 90 degrees, and an angle between the second vector and the fourth vector is greater than zero degrees and less than 90 degrees.
Methods, computer-program products 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.
C09B 23/08 - Methine or polymethine dyes, e.g. cyanine dyes characterised by the methine chain containing an odd number of CH groups more than three CH groups, e.g. polycarbocyanines
G01N 33/532 - Production of labelled immunochemicals
G01N 33/58 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
7.
SYSTEM AND METHOD FOR CELL-OF-ORIGIN CLASSIFICATION BASED ON INTERPRETABLE CELLULAR FEATURES
A method of classifying a tissue sample by a classification system includes identifying, by the classification system, a plurality of tiles corresponding to whole-slide image data of the tissue sample; generating, by the classification system, a plurality of semantic masks corresponding to the plurality of tiles, each one of the plurality of semantic masks identifying a cell boundary and a cell type of each cell within a corresponding tile of the plurality of tiles; generating, by the classification system, a plurality of cellular features for each tile of the plurality of tiles based on a corresponding one of the plurality of semantic masks; and classifying, by the classification system, the tissue sample based on the plurality of cellular features for each one of the plurality of tiles.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A scanning device for scanning a target area of a sample, the scanning device comprising: a transportation system arranged to support the sample and move the target area of the sample between a first location and a second location; a casing configured to support the transportation system, the casing mechanically coupled to the transportation system such that movement of the transportation system induces oscillatory motion in the casing; a controller configured to: accelerate the transportation system in a first direction, from the first location to the second location, causing a reaction force in the second direction and initial oscillatory motion of the casing in the second direction; and decelerate the transportation system in the first direction to bring the transportation system to a rest when the sample is in the second location, producing a reaction force on the casing in the first direction which balances oscillatory movement of the casing in the second direction, to bring the casing to rest.
Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a slice of specimen. Particularly, aspects of the present disclosure are directed to accessing a multiplex immunofluorescence image of a slice of specimen comprising a first channel for a nuclei marker and a second channel for an epithelial tumor marker, wherein the slice of specimen comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine-learning model; receiving an output of the machine-learning model corresponding to a prediction that the multiplex immunofluorescence image includes one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine-learning model; and outputting the mask for the subsequent image processing.
Disclosed is a device for contactlessly mixing fluid present on the upper surface of the slide, where the device comprises a first nozzle array and a second nozzle array, the first nozzle array adapted to impart a bulk fluid flow to the fluid present on the upper surface of the slide, and the second nozzle array adapted to impart at least a first regional fluid flow to at least a portion of the fluid present on the upper surface of the slide
A microscope scanning apparatus is provided comprising a detector array for obtaining an image from a sample and a sample holder adapted to hold the sample when in use and to move relative to the detector array along a scan path. A controller is further provided to monitor the position of the sample holder relative to the detector array and to trigger image capture by the detector array in accordance with said monitored position.
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 domain swap by accessing an image from a first domain and is processed using a machine learning model to generate a virtual synthetic image in a second domain. This approach can eliminate or reduce the need to separately collect an image in the second domain, which can save time and cost. Leveraging tools that are available in the second domain to perform image processing on the virtual synthetic image. Results or analysis from the image processing in the second domain can then be directly applied to the first domain and to assess the image further. Since spatial reference points (size, scale, view etc.) are same, pixels identifying a boundary of a region depicted in the virtual synthetic image are the same pixels in the first domain.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
14.
MATERIALS AND METHODS FOR BLEACHING MELANIN-PIGMENTED TISSUES
A method of depigmenting melanin-pigmented samples is provided. The sample is incubated in the presence of a hydrogen peroxide-based solution at a temperature less than 65° C. for up to 180 minutes. Times, temperatures, and concentrations of hydrogen peroxide that appropriately balance extent of depigmentation with maintenance of cellular morphology and sample retention are also disclosed.
The present disclosure relates to stain unmixing of digital pathology images by determining initial color vectors associated with digital pathology stains (or chromogens) from pure-color digital pathology images. The determined color vectors may be fine-tuned or adjusted to help improve the stain unmixing performance. The adjustment may be performed via the interface and/or automated technique that, based on a real multiplex image and one or more synthetic singleplex images, perform adjustments to the color vectors. These adjusted color vectors may be further leveraged for stain unmixing of a given multiplex image. Additionally, the disclosure provides techniques to generate synthetic pixels and the associated color vectors, a recommended stain to be added to a multiplex image and/or generation of multiplex images from one or more digital pathology images based on the targeted color vectors.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
Disclosed herein are embodiments of imaging biological specimens. An imaging system can include a microscope for directly viewing the biological specimen and a multi-spectral imaging apparatus for outputting digitally enhanced images, near-video rate imaging, and/or videos of the specimen. An imaging system can include a digital scanner that digitally processes images to produce a composite image with enhanced color contrast of features of interest.
H04N 23/11 - Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
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 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
THE FRANCIS CRICK INSTITUTE LIMITED (United Kingdom)
THE ROYAL MARSDEN NHS FOUNDATION TRUST (United Kingdom)
Inventor
Alexander, Nelson
Gallegos, Lisa
Hanley, Brian
Turajlic, Samra
Abstract
Disclosed herein is a method of analyzing flow cytometry data for cells derived from homogenized whole tumor samples. In some embodiments, the present disclosure is directed to a cytometric assay for distinguishing between tumor cells expressing a cell proliferation marker and normal cells expressing the cell proliferation marker. In some embodiments, the present disclosure is also directed to quantifying a percentage of normal cells expressing a cell proliferation marker and a percentage of tumor cells expressing the cell proliferation marker.
A digital pathology image collected using bright-field imaging that depicts a slide with a stained sample slice is accessed. A stain intensity that corresponds to at least part of the digital pathology image is detected. A biomarker-intensity-prediction function that linearly relates predicted biomarker-intensity levels to detected stain intensities is accessed. A non-linear confidence function is accessed that relates confidences of a predicted biomarker intensity to the detected intensities of the stain. A predicted biomarker intensity is generated for the at least part of the slide using the detected stain intensity that corresponds to the at least part of the slide and the linear biomarker-intensity-prediction function. A confidence metric for the predicted biomarker intensity is generated using the detected stain intensity that corresponds to the at least part of the slide and based on the confidence function. A result based on the predicted biomarker intensity and the confidence metric is output.
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.
A system and method for dispense characterization is disclosed. According to particular embodiments of the dispense characterization system and method, volumes of dispensed liquids can be determined. In more particular embodiments, additional characteristics, and combinations of characteristics of a liquid dispensing event can be determined. Examples of additional characteristics that can be determined include the shape of the dispensing event, the velocity of the dispensing event, and the trajectory of the dispensing event. The dispense characterization system and method can be employed in automated biological sample analysis systems, and are particularly suited for monitoring liquid reagent dispensing events that deliver liquid reagents to a surface of a microscope slide holding a biological sample.
G01N 35/00 - Automatic analysis not limited to methods or materials provided for in any single one of groups ; Handling materials therefor
22.
COMPOSITIONS AND METHODS FOR SIMULTANEOUS INACTIVATION OF ALKALINE PHOSPHATASE AND PEROXIDASE ENZYMES DURING AUTOMATED MULTIPLEX TISSUE STAINING ASSAYS
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.
The present disclosure provides for image processing systems and methods for analyzing digital images of biological samples stained for the presence of protein and/or nucleic acid biomarkers and detecting and quantifying signals corresponding to one or more biomarkers. The present disclosure also provides systems and methods for the clinical interpretation of dual ISH slides where the cells to score are selected (e.g. by using one or more cell detection and identification algorithms. By detecting, identifying, and selecting cells for assessment, it is believed that subjectivity is reduced or eliminated. The systems and methods also allow for an increased number of cells to be considered for scoring as compared with manual dot counting methods, thereby increasing detection sensitivity, ultimately enabling improved patient care and treatment.
A method of displaying biological image data in an interface application on a digital pathology analysis system includes: receiving a first user input pertaining to a first patient case; contacting a remote server for one or more stored biological image tiles corresponding to the received first user input, wherein the stored biological image tiles are derived from scans of microscope slides having one or more biological samples disposed thereon, the biological samples stained with hematoxylin and eosin or stained to identify presence of one or more biomarkers; receiving location information for the one or more stored biological image tiles corresponding to the first user input; retrieving the one or more stored biological image tiles pertaining to the first user input based on the received location information; and visualizing the retrieved one or more biological image tiles pertaining to the first user input.
A method for forming an image of a sample mounted on a slide includes: forming, using an imaging system, a first image of a first swathe of a plurality of swathes, the first swathe being positionally constrained within an aperture in a movable stage configured to move along first and second slide movement axes relative to the imaging system; using a copy holder moving system to move the movable stage, wherein the copy holder includes: a plurality of apertures each configured to hold a respective slide; an indexing arm for holding the copy holder; and an indexing motor configured to move the indexing arm along an indexing axis that is nonparallel with respect to both the first and second slide movement axes; and forming, using the imaging system, a second image of a second swathe of the plurality of swathes.
Presented herein are methods of improving the consistency of staining with a counterstain. In some embodiments, the method makes use of an automated specimen processing apparatus or other staining device. In some embodiments, the staining methods are applied manually. In some embodiments, the counterstain includes eosin.
Aspects of the present disclosure pertain to systems and methods for enhancing brightfield or darkfield images to better enable nucleus detection. In some embodiments, the systems and methods described herein are useful for identifying membrane stain biomarkers as well as nuclear/cytoplasm stain biomarkers in stained images of biological samples. In some embodiments, the presently disclosed systems and methods enable quick and accurate nucleus detection in stained images of biological samples, especially for original stained images of biological samples where the nuclei appear faint.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
30.
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.
G06F 16/535 - Filtering based on additional data, e.g. user or group profiles
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
G06T 3/40 - Scaling of a whole image or part thereof
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
31.
GRAM STAINING METHOD WITH IMPROVED DECOLORIZATION OF THE CRYSTAL VIOLET-IODINE COMPLEX FROM GRAM NEGATIVE BACTERIA
Provided herein are methods of staining biological material for the purpose of detecting, and in some examples also identifying, microorganisms. Methods of Gram staining bacteria using a slow-acting decolorizing formulation, such as one that includes 1,2-propandiol or ethylene glycol, can be used to extend the time of the decolorizing step, and thus permit automation of the Gram staining method. Also provided are compositions and kits for performing automated Gram staining on microscope slides.
C12Q 1/04 - Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
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.
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
33.
SYSTEMS AND METHODS FOR MONITORING TISSUE SAMPLE PROCESSING
A tissue sample that has been removed from a subject can be evaluated. A change in speed of the energy traveling through the sample is evaluated to monitor changes in the biological sample during processing. The monitoring can detect movement of fluid with the sample and cross-linking. A system for performing the method can include a transmitter that outputs the energy and a receiver configured to detect the transmitted energy.
The present disclosure is directed to epitope-tagged antibodies, as well as methods of employing the epitope-tagged antibodies for detecting one or more targets in a biological sample, e.g. a tissue sample.
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
C07K 16/18 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes
35.
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 10/77 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
36.
MACHINE-LEARNING TECHNIQUES FOR PREDICTING PHENOTYPES IN DUPLEX DIGITAL PATHOLOGY IMAGES
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 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
37.
MATERIALS AND METHODS FOR EVALUATION OF ANTIGEN PRESENTATION MACHINERY COMPONENTS AND USES THEREOF
Disclosed herein are compositions, systems, and methods for identifying subjects who may be responsive to MHC-I-dependent immunotherapeutic agents based upon the expression of the components of the antigen presentation machinery and, in particular, the expression of the constituent elements of the transporter associated with antigen processing complex and the major histocompatibility complex class I.
G01N 33/574 - Immunoassay; Biospecific binding assay; Materials therefor for cancer
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
The disclosure generally relates to the preparation of representative samples from clinical samples, e.g., tumors (whole or in part), lymph nodes, metastases, cysts, polyps, or a combination or portion thereof, using mechanical and/or biochemical dissociation methods to homogenize intact samples or large portions thereof. The resulting homogenate provides the ability to obtain a correct representative sample despite spatial heterogeneity within the sample, increasing detection likelihood of low prevalence subclones, and is suitable for use in various diagnostic assays as well as the production of therapeutics, especially “personalized” anti-tumor vaccines or immune cell based therapies.
A method may include identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample. Each mitotic figure of the plurality of mitotic figures may correspond to a tumor cell that is undergoing mitosis. A mitotic metric quantifying a spatial distribution of the plurality of mitotic figures within the biological sample may be determined based on the plurality of mitotic figures in the biological sample. A tumor grade for the tumor tissue present in the biological sample may be determined based on the mitotic metric. In some cases, at least one of a disease diagnosis, a disease progression, a disease burden, a treatment response, and a survival prognosis for a patient associated with the biological sample may be determined based on the tumor grade. Related systems and computer program products are also provided.
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 - Methods or arrangements for marking the record carrier in digital fashion
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.
The present disclosure relates to systems and methods for determining a consensus location and label for a set of annotations associated with an object or region within an image. An annotation-processing system can access a plurality of annotations associated with the image depicting at least part of a biological sample. The annotation-processing system can determine a consensus location for a set of annotations that are positioned in different locations within a region of the image. At the determined consensus location, a consensus label can be determined for the set of annotations that identify different targeted types of biological structures. The consensus labels across different locations can be used to generate ground-truth labels for the image. The ground-truth labels can be used to train a machine-learning model configured to predict different types of biological structures in digital pathology images.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
In some embodiments, the present disclosure is directed to coatings or thin films comprising a dye or stain embedded within a matrix, e.g. a polymer matrix.
The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
G06F 18/231 - Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram
G06F 18/243 - Classification techniques relating to the number of classes
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
45.
AUTO-FOCUS METHODS AND SYSTEMS FOR MULTI-SPECTRAL IMAGING
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.
Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.
Embodiments disclosed herein generally relate to expression-level prediction for digital pathology images. Particularly, aspects of the present disclosure are directed to accessing a duplex immunohistochemistry image of a slice of specimen, wherein the duplex immunohistochemistry image comprises a depiction of cells associated with a first biomarker and/or a second biomarker corresponding to a disease; generating, from the duplex immunohistochemistry image, a first synthetic image depicting the first biomarker and a second synthetic image depicting the second biomarker; determining a set of features representing pixel intensities of the depiction of cells in the first synthetic image and the second synthetic image; processing the set of features using a trained machine learning model; and outputting a result that corresponds to a predicted characterization of the specimen with respect to the disease based on an output of the processing corresponding to a predicted expression level of the first biomarker and the second biomarker.
A method of predicting overall survivability of a patient by a prediction system based on machine learning includes receiving, by the prediction system including a processor and a memory, a plurality of input modalities corresponding to the patient, the input modalities being of different types from one another, generating, by the prediction system, a plurality of intermediate features based on the plurality of input modalities, each input modality of the plurality of input modalities corresponding to one or more features of the plurality of intermediate features, and determining, by the prediction system, a survivability score corresponding to an overall survivability of the patient based on a fusion of the plurality of intermediate features.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
49.
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 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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.
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 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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.
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 - Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
G01N 33/542 - Immunoassay; Biospecific binding assay; Materials therefor with immune complex formed in liquid phase with steric inhibition or signal modification, e.g. fluorescent quenching
G01N 33/543 - Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
61.
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 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G01N 33/574 - Immunoassay; Biospecific binding assay; Materials therefor for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
63.
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.
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.
A gripper system includes: a first gripper finger having a first groove, a second groove, and a third groove; and a second gripper finger having a first groove, a second groove, and a third groove, wherein the gripper is configured to apply a first compression force against length edges of a slide in a widthwise direction at the first grooves, the gripper is configured to apply a second compression force against width edges of the slide in a lengthwise direction at the second grooves, the gripper is configured to apply a third compression force against width edges of the slide in a lengthwise direction at the third grooves, and the third compression force against the width edges of the slide in the lengthwise direction is an orientation in which the slide is rotated perpendicular to an orientation corresponding to a gripping position of the second grooves.
Embodiments disclosed herein generally relate to representative datasets for biomedical machine learning models. Particularly, aspects of the present disclosure are directed to identifying a representative distribution of characteristics for a disease, generating a dataset comprising a set of biomedical images, wherein the dataset has a distribution of the characteristics that corresponds to the representative distribution of the characteristics for the disease, processing the dataset using a trained machine learning model, and outputting a result of the processing, wherein the result corresponds to a prediction that a biomedical image of the dataset includes a depiction of a set of tumor cells or other structural and/or functional biological entities associated with the disease, the biomedical image is associated with a diagnosis of the disease, the biomedical image is associated with a classification of the disease, and/or the biomedical image is associated with a prognosis for the disease.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
67.
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.
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 - Immunoassay; Biospecific binding assay; Materials therefor for cancer
C07K 16/22 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against growth factors
C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
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 - Heterocyclic compounds, e.g. containing phosphorus as a ring hetero atom containing systems of two or more relevant hetero rings condensed among themselves or condensed with a common carbocyclic ring or ring system, with or without other non-condensed hetero rings
C07F 9/6558 - Heterocyclic compounds, e.g. containing phosphorus as a ring hetero atom containing at least two different or differently substituted hetero rings neither condensed among themselves nor condensed with a common carbocyclic ring or ring system
C07F 9/12 - Esters of phosphoric acids with hydroxyaryl compounds
C07D 209/14 - Radicals substituted by nitrogen atoms, not forming part of a nitro radical
C07H 15/203 - Monocyclic carbocyclic rings other than cyclohexane rings; Bicyclic carbocyclic ring systems
C07H 15/26 - Acyclic or carbocyclic radicals, substituted by hetero rings
C12Q 1/42 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving hydrolase involving phosphatase
C07H 15/207 - Cyclohexane rings not substituted by nitrogen atoms, e.g. kasugamycins
G01N 33/58 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
C07D 403/06 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a carbon chain containing only aliphatic carbon atoms
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 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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.
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 - Normal steroids containing carbon, hydrogen, halogen, or oxygen, substituted in position 17 by a lactone ring
G01N 33/58 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additives; Targeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
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.
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 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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.
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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 - Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection
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.
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
82.
CELL CLASSIFICATION USING CENTER EMPHASIS OF A FEATURE MAP
Techniques described herein include, for example, generating a feature map for an input image, generating a plurality of concentric crops of the feature map, and generating an output vector that represents a characteristic of a structure depicted in a center region of the input image using the plurality of concentric crops. Generating the output vector may include, for example, aggregating sets of output features generated from the plurality of concentric crops, and several methods of aggregating are described. Applications to classification of a structure depicted in the center region of the input image are also described.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Embodiments described herein pertain to systems and methods for imaging. An imaging system may include a castellated optical element, a CMOS image sensor, and color filtering elements. The CMOS image sensor may include focus areas, a line-scan area, a 2D imaging area, and look-ahead gaps. The imaging system may be configured scan and capture bi-directionally, forward-focused brightfield and fluorescence images of one or more slides comprising at least one biological material.
A slide carrier includes: a base support; and a slide platform having a surface that is parallel to a first plane defined by a first vector and a second vector, wherein a vector extending in a direction opposite to the direction of gravity is normal with respect to a second plane defined by a third vector and a fourth vector, an angle between the first vector and the third vector is greater than zero degrees and less than 90 degrees, and an angle between the second vector and the fourth vector is greater than zero degrees and less than 90 degrees.
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.
G06F 16/535 - Filtering based on additional data, e.g. user or group profiles
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH (USA)
Inventor
Ahanonu, Eze, O.
Mcmahon, Sara, M.
Shanmugam, Kandavel
Sinicrope, Frank, A.
Williams, Crystal, Sue
Yan, Dongyao
Zhang, Wenjun
Abstract
Scoring functions for predicting response of a dMMR and/or MSI-H colorectal tumor to a PD-1 axis-directed therapy are disclosed, as well as methods and systems for evaluating tissue samples for the presence of feature metrics useful in computing such scoring functions. The scoring functions integrate one or more spatial relationships between cell types into a numerical indication of the likelihood that the tumor will respond to the PD-1 axis-directed therapy. Based on the output of the scoring function, a subject may then be selected to receive a PD-1 axis-directed therapy (if the scoring function indicates a sufficient likelihood of positive response) or an alternative therapy (if the scoring function indicates an insufficient likelihood of positive response).
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.
G02B 21/34 - Microscope slides, e.g. mounting specimens on microscope slides
B01L 7/00 - Heating or cooling apparatus; Heat insulating devices
B05C 11/02 - Apparatus for spreading or distributing liquids or other fluent materials already applied to a surface; Control of the thickness of a coating
G01N 35/00 - Automatic analysis not limited to methods or materials provided for in any single one of groups ; Handling materials therefor
88.
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 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
89.
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 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
A61K 31/4745 - Quinolines; Isoquinolines ortho- or peri-condensed with heterocyclic ring systems condensed with ring systems having nitrogen as a ring hetero atom, e.g. phenanthrolines
91.
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.
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 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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.
A method of depigmenting melanin-pigmented samples is provided. The sample is incubated in the presence of a hydrogen peroxide-based solution at a temperature less than 65 °C for up to 180 minutes. Times, temperatures, and concentrations of hydrogen peroxide that appropriately balance extent of depigmentation with maintenance of cellular morphology and sample retention are also disclosed.
The present disclosure relates to techniques for 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.
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.
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