The invention relates to a method and system for optical product authentication, in which a product is labeled with optically active particles, a reference image is recorded in a registration step and a recognition image of the optically active particles is recorded in a recognition step. The product is then authenticated by comparing image data or a coding derived from image data in the registration step versus the recognition step.

This disclosure describes single-use test cartridges, cell analyzer apparatus, and methods for automatically performing microscopic cell analysis tasks, such as counting blood cells in biological samples. A small unmeasured quantity of a biological sample such as whole blood is placed in the disposable test cartridge which is then inserted into the cell analyzer. The analyzer isolates a precise volume of the biological sample, mixes it with self-contained reagents and transfers the entire volume to an imaging chamber. The geometry of the imaging chamber is chosen to maintain the uniformity of the mixture, and to prevent cells from crowding or clumping, when it is transferred into the imaging chamber. Images of essentially all of the cellular components within the imaging chamber are analyzed to obtain counts per unit volume. The devices, apparatus and methods described may be used to analyze a small quantity of whole blood to obtain counts per unit volume of red blood cells, white blood cells, including sub-groups of white cells, platelets and measurements related to these bodies.

A method of determining the location and quantity of mitochondrial fission, fusion and depolarisation events that occur in a cell is provided. Using a three-dimensional time lapse image sequence of a cell, the method identifies which of the mitochondria in a cell had depolarised or undergone fission or fusion in the interval between the acquisition of the earlier and later images, indicates the locations of the fission, fusion and depolarisation events, and generates a count of the number of mitochondrial fission, fusion and/or depolarisation events. The method can be used to diagnose a disease or condition associated with mitochondrial dysfunction, such as neurodegenerative disease, cancer or ischaemic heart disease. The method can further be used to screen a compound or composition for use in preventing or treating a disease or condition associated with mitochondrial dysfunction. The method can be computer-implemented, and a computer program product is provided.

An optical method of characterizing an object comprises providing an object to be characterized, the object having at least one nanoscale feature; illuminating the object with coherent plane wave optical radiation having a wavelength larger than the nanoscale feature; capturing a diffraction intensity pattern of the radiation which is scattered by the object; supplying the diffraction intensity pattern to a neural network trained with a training set of diffraction intensity patterns corresponding to other objects with a same nanoscale feature as the object to be characterized, the neural network configured to recover information about the object from the diffraction intensity pattern; and making a characterization of the object based on the recovered information.

An automatic abnormal cell recognition method, the method including: 1) scanning a slide using a digital pathological scanner and obtaining a cytological slide image; 2) obtaining a set of centroid coordinates of all nuclei that is denoted as CentroidOfNucleus by automatically localizing nuclei of all cells in the cytological slide image using a feature fusion based localizing method; 3) obtaining a set of cell square region of interest (ROI) images that are denoted as ROI_images; 4) grouping all cell images in the ROI_images into different groups based on sampling without replacement, where each group contains ROW×COLUMN cell images with preset ROW and COLUMN parameters; obtaining a set of splice images; and 5) classifying all cell images in the splice image simultaneously by using the splice image as an input of a trained deep neural network; and recognizing cells classified as abnormal categories.

The present disclosure presents systems and methods of tagging TRISO-fueled pebbles. One such method comprises acquiring an ionizing radiation image of a TRISO-fueled pebble; analyzing, using a machine learning algorithm, the acquired image of the TRISO-fueled pebble to identify a unique pattern of particle distributions that is visible in the acquired image of the TRISO-fueled pebble; deriving a TRISO-particle distribution fingerprint for the TRISO-fueled pebble that corresponds to the unique pattern of particle distributions; assigning an individual identifier to the TRISO-fueled pebble that corresponds to a TRISO-particle distribution fingerprint; and storing the TRISO-particle distribution fingerprint and the individual identifier for the TRISO-fueled pebble in an image database, wherein the image database stores a plurality of TRISO-particle distribution fingerprints and individual identifiers for a plurality of TRISO-fueled pebbles. Other systems and methods are also presented.

An information processing device detects a cell candidate region for determining a unity of a cell from a vessel image obtained by imaging a vessel in which the cell is seeded and includes at least one processor. The processor performs an acquisition process of acquiring the vessel image, performs a detection process of detecting a cell region including the cell and a cell-like region including an object similar to the cell as the cell candidate regions from the acquired vessel image, and an output process of outputting information indicating the detected cell candidate regions.

An object classification system for classifying objects is described. The system comprises an imaging region adapted for irradiating an object of interest, an arrayed detector, and a mixing unit configured for mixing the irradiation stemming from the object of interest by reflecting or scattering on average at least three times the irradiation after its interaction with the object of interest and prior to said detection.

A cell image processing system includes a cell image processing device, a display terminal, and an input unit. The cell image processing device includes an acquisition unit that acquires cell image related data, a storage unit, a data tree creating unit that creates a data tree, and a control unit that performs control to popup display the cell image related data selected by first selection by the input unit in the data tree.

A scanning electron microscope (SEM) device includes: an electron beam source configured to emit an electron beam; a lens unit disposed between the electron beam source and a stage configured to seat an object including structures having a pattern is seated, and including a scanning coil, the scanning coil configured to generate an electromagnetic field to provide a lens, and an astigmatism adjuster; and a control unit. The control unit is configured to change a working distance between the lens unit and the object to obtain a plurality of original images, obtain a pattern image, in which the structures appear, and a plurality of kernel images, in which a distribution of the electron beam on the object appears, from the plurality of original images, and control the astigmatism adjuster to adjust the focus and the astigmatism of the lens unit using feature values extracted from the plurality of kernel images.