Abstract: Provided are systems and methods for imaging with compensation functions. An imaging device comprises a plurality of light source sets, of which two or more light source sets emit light of a same specific spectral range to compensate intensity differences among different spectral ranges. The imaging device can be integrated with a mobile device. A method comprises a subtraction procedure to compensate ambient light effect, a normalization procedure to compensate incidence angle effect, a 3D reconstruction procedure to compensate distance effect, or any combination of these procedures. The method is performed at an imaging device comprising a controller. At least one program is non-transiently stored in the controller and executable by the controller.

Abstract: Described herein are methods for co-expression analysis of multiple markers in a tissue sample comprising: computing a heat map of marker expression for each of a plurality of single marker channel images, wherein each of the plurality of single marker channel images comprise a single marker; identifying one or more candidate regions of interest in each heat map of marker expression; computing overlay masks comprising the identified one or more candidate regions of interest from each heat map of marker expression; determining one or more co-localized regions of interest from the overlay masks; mapping the one or more co-localized regions of interest to a same coordinate position in each of the plurality of single marker channel images; and estimating a number of cells in at least one of the determined one or more co-localized regions of interest in each of the plurality of single marker channel images.

Abstract: An apparatus for analyzing a subject including a hyperspectral image module is provided. It is used to identify a suspect region of a subject by using a hyperspectral sensor (for obtaining a hyperspectral image of the subject), a control computer including a processor unit (PU) and a computer readable memory (CRM) (for controlling and is in electronic communication with the sensor), a control software module including instructions stored in the CRM and executed by the PU (for controlling said at least one operating parameter of the sensor), a spectral calibrator module including instructions stored in the CRM and executed by the PU (for applying a wavelength dependent spectral calibration standard constructed for the sensor to a hyperspectral image), and a light source for illuminating the subject. An optional contact probe module is used to collect a signal of the suspect region for medical diagnosis.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and method of displaying of multiple simultaneous views of a same region of a biological tissue sample. Logical instructions are executed by a processor to perform operations such as receiving a plurality of images of the biological tissue sample, converting the plurality of images to a common reference frame based on the individual metadata of each image, and arranging the plurality of images into a display pattern for simultaneous viewing of different aspects of the imaged biological tissue sample on a display screen. The plurality of images is produced by preprocessing images of the biological tissue sample. Each image shows a view mode of a same region of the biological tissue sample, and each image contains metadata that describe spatial orientation, such as the translation, rotation, and magnification, of the image to bring the plurality of images to a common view.

Abstract: The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Abstract: 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.

Abstract: An apparatus for analyzing a subject including a hyperspectral image module is provided. It is used to identify a suspect region of a subject by using a hyperspectral sensor (for obtaining a hyperspectral image of the subject), a control computer including a processor unit (PU) and a computer readable memory (CRM) (for controlling and is in electronic communication with the sensor), a control software module including instructions stored in the CRM and executed by the PU (for controlling said at least one operating parameter of the sensor), a spectral calibrator module including instructions stored in the CRM and executed by the PU (for applying a wavelength dependent spectral calibration standard constructed for the sensor to a hyperspectral image), and a light source for illuminating the subject. An optional contact probe module is used to collect a signal of the suspect region for medical diagnosis.

Abstract: Systems and methods for imaging using a plurality of light sources is provided. An imaging device comprises a housing, a lens within the housing, a plurality of light source sets attached or integrated into the housing, a two-dimensional pixelated detector within the housing in optical communication with the lens, memory and a controller. Each respective light source set comprises a plurality of lights uniformly radially distributed about the lens. A first light source set in the plurality sets emits light substantially limited to a first spectral range. A second light source set in the plurality of sets emits light substantially limited to a second spectral range other than the first range. At least one program non-transiently stored in the memory and executable by the controller sequentially and independently fired each light source set while collecting light using the pixelated detector. An imaging device can be integrated with a mobile device.

Abstract: A system and method of displaying of multiple simultaneous views of a same region of a biological tissue sample. Logical instructions are executed by a processor to perform operations such as receiving a plurality of images of the biological tissue sample, converting the plurality of images to a common reference frame based on the individual metadata of each image, and arranging the plurality of images into a display pattern for simultaneous viewing of different aspects of the imaged biological tissue sample on a display screen. The plurality of images is produced by preprocessing images of the biological tissue sample. Each image shows a view mode of a same region of the biological tissue sample, and each image contains metadata that describe spatial orientation, such as the translation, rotation, and magnification, of the image to bring the plurality of images to a common view.

Abstract: The subject disclosure presents systems and methods for improved meso-dissection of biological specimens and tissue slides including importing one or more reference slides with annotations, using inter-marker registration algorithms to automatically map the annotations to an image of a milling slide, and dissecting the annotated tissue from the selected regions in the milling slide for analysis, while concurrently tracking the data and analysis using unique identifiers such as bar codes.

Abstract: The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Abstract: Systems and methods disclosed herein describe a platform that automatically creates and executes a scoring guide for use in anatomical pathology. The platform can employ a fully-automated workflow for clustering the biological objects of interest and for providing cell-by-cell read-outs of heterogeneous tumor biomarkers based on their stain appearance. The platform can include a module for automatically creating and storing a scoring guide in a training database based on training digital images (240, 250), and an object classification module that executes the scoring guide when presented with new digital images to be scored pursuant to the scoring guide (299).

Abstract: At a central controlling system, a composite map is constructed based on a previous dataset that includes (A) images of a target obtained by imaging devices at a first time, and (B) respective meta data associated with the images. Imaging devices collect image data during a second time after the first time. In accordance with the composite map, the imaging devices obtain images of the target and associate meta data with the images. The images and respective meta data are communicated to the central controlling system. The positions and orientations of the imaging devices when the respective images were obtained, and when the images were obtained, are used to index the images against the target, thereby aggregating multi-dimensional data for the target. Temporal information about a characteristic of the target over time is then extracted from the aggregated multi-dimensional data.

Abstract: Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.

Abstract: At a first computer-enabled imaging device in a plurality of computer-enabled imaging devices, a first workflow including time-stamped images of a region of interest is obtained during a first time interval, and first meta data is associated with the second workflow. The first meta data includes positional and orientation data, the first time interval, and an identity of the first computer-enabled imaging device. Control signals are generated based on a characteristic of the first workflow, and then communicated to a second computer-enabled imaging device. In accordance with the control signals, the second computer-enabled imaging device obtains a second workflow including time-stamped images of the region of interest, and associates second meta data with the second workflow. A central system, or any computer-enabled imaging device in the plurality, consolidates the first and second workflows into a consolidated workflow for the region of interest using the first and second meta data.

Abstract: A digital pathology system and associated method and computer program product provide a quantitative analysis of entire tissue slides as well as intuitive, effective, fast, and precise quantification of biomarker expressions across relevant areas of the entire tissue slides. The digital pathology system enables a novel workflow that allows the user to efficiently outline clinically relevant morphology in its entirety, including solid tumor areas. Quantitative analysis results are then efficiently and intuitively provided to the user for all tissue content (i.e., millions of cells) within seconds. This efficiency is made possible by a pre-computation step that computes and stores image analysis results for later retrieval. Visualizing vast amount of data effectively is another component of the system that provides information and confidence to the user about the biomarker expression levels.