An information processing unit includes: a diagnostic image input section that inputs the diagnostic image; an operation information obtaining section that obtains display operation history information representing an operation history of a user who controls displaying of the diagnostic image; a query image generation section that extracts a predetermined region of the input diagnostic image to generate a query image; a diagnosed image obtaining section that supplies the generated query image and the display operation history information to a diagnosed image search unit and obtains the diagnosed image obtained as a search result by the diagnosed image search unit; and a display control section that displays the diagnostic image and the obtained diagnosed image for comparison.

In an example embodiment, may be embodied in a data analysis apparatus comprises a control unit configured to provide data representative of a three dimensional image, the three dimensional image including at least a three dimensional coordinate space which includes at least one plane that divides the three dimensional coordinate space into at least two regions, a display unit configured to produce the three dimensional image based on the data representative of the three dimensional image, and an input unit configured to provide data representative of at least one of a movement and a position of the at least one plane. In other example embodiments, the present disclosure may be embodied in a data analysis server, a data analysis system, and/or a computer readable medium.

This document describes methods, systems and computer program products directed to imaging blood cells. The subject matter described in this document can be embodied in a method of classifying white blood cells (WBCs) in a biological sample on a substrate. The method includes acquiring, by an image acquisition device, a plurality of images of a first location on the substrate, and classifying, by a processor, objects in the plurality of images into WBC classification groups. The method also includes identifying, by a processor, objects from at least some classification groups, as unclassified objects, and displaying, on a user interface, the unclassified objects and at least some of the classified objects.

The disclosure concerns a method for assessing muscular dystrophy-linked protein expression in muscle fibers using digital image analysis of tissue. The method relates to assessing disease severity in individuals with muscular dystrophy. Muscle tissue samples are obtained from patients submitted for evaluation and processed to produce tissue sections mounted on glass slides which have been stained for a muscular dystrophy-linked protein. Digital images of the stained tissue sections are generated and analyzed by applying an algorithm process implemented by a computer to the images. The algorithm process extracts the morphometric and staining features of the muscular dystrophy-linked protein staining in the tissue, and parameters relating to these features are used to score the disease status for each patient submitted for evaluation. The score of disease status is ultimately used to infer disease severity, monitor the efficacy of a therapeutic approach, or select patients as candidates for a therapeutic approach.

A method for refining label information, which is performed by at least one computing device is disclosed. The method includes acquiring a pathology slide image including a plurality of patches, inferring a plurality of label information items for the plurality of patches included in the acquired pathology slide image using a machine learning model, applying the inferred plurality of label information items to the pathology slide image, and providing the pathology slide image applied with the inferred plurality of label information items to an annotator terminal.

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

The present disclosure relates to an apparatus including one or more processors configured to receive a digitized image of a region of tissue demonstrating a disease, and containing cellular structures represented in the digitized image, each of the cellular structures being associated with a cell category of a plurality of cell categories; select a cellular structure of the cellular structures based on the cell category for the cellular structure; for the cellular structure selected, compute a set of contextual features; assign, based on the set of contextual features, the cellular structure to at least one cluster of a plurality of clusters; compute cluster features, the cluster features describing characteristics of the at least one cluster of the plurality of clusters; and generate a prediction that describes a pathologic or phenotypic state of the disease based, at least in part, on the cluster features and/or the set of contextual features.

An image diagnosis method comprises a step of acquiring an image including at least one of a tissue and a cell as an element, a step of classifying, for each partial image that is a part of the image, a property of the element included in the partial image, and a step of sorting the image into any one of benign indicating that no lesion element is present, malignant indicating that a lesion element is present, and follow-up based on classification results of the plurality of partial images.

Systems and methods are provided for classifying microbial cells according to morphological features of microcolonies. A dark-field objective is employed to acquire a dark-field image of a microcolony during a microcolony growth phase that is characterized by phenotypic expression of microcolony morphological features which evolve with time and are differentiated among classes of microbial cell types. The dark-field image is processed to classify the microcolony according to two or more microbial cell types, such as Gram status and/or speciation. The dark-field objective may have a numerical aperture selected to facilitate the imaging of microcolony morphological features, residing, for example, between 0.15 and 0.35. A set of dark-field images of a microcolony may be collected during the microcolony growth phase and processed to classify the microcolony. Classification may be performed according to a temporal ordering of the dark-field images, for example, using a recurrent neural network.