A method for classifying cells densities by cell images being input into artificial computer intelligence obtains positional information of all central points of all groups of first encoding features generated when training a model of convolutional neural network and ranges of densities of images of biological cells represented by different central points. The method inputs a test image of the biological cells into a trained model of the convolutional neural network to encode the test image, to obtain a second encoding feature. The method also determines a central point nearest to the second encoding feature according to the positional information. The method determines a range of densities of the test image according to the ranges of densities of the images represented by different central points and the central point nearest to the second encoding feature. An electronic device and a non-transitory storage medium are also disclosed.

Provided are methods, devices, and systems for a loop-mediated isothermal amplification (LAMP) reactions for detecting the presence of genetic material. A LAMP device includes an enclosure having outer walls configured to define an internal space. The LAMP device also includes a tray assembly configured to support a reaction vessel configured for holding a sample for amplification. The LAMP device also includes a plurality of light emitters configured to illuminate the reaction vessel for exciting a fluorescence of the sample in the reaction vessel. The LAMP device also includes an imaging device configured to obtain an image of the sample for determining a quantity of the fluorescence in the sample. The LAMP device also includes a controller configured to regulate a temperature of the tray assembly and instruct the imaging device to obtain the image for determining the quantity of the fluorescence in the sample.

A cell recovery apparatus includes an observation apparatus for observing the inside of a medium in a container via an insertion section to be inserted into the medium, and a recovery apparatus that is positioned relative to a predetermined region to be observed by the observation apparatus and recovers a cell in the medium from the container.

Systems and methods here may be used for analyzing images of gemstones to automatically assign a haziness and/or fluorescence grade to the gemstone using contrast analysis on pixelated, digital images of the gemstones.

An image capturing system includes a camera unit that captures an image of a cell; a display unit; an input unit that receives input from an operator regarding a selection operation on the cell; an analyzing unit that analyzes the image corresponding to the cell to which the selection operation is given and extracts a feature amount of the cell; and a specifying unit that specifies a recommended cell. The specifying unit sets a parameter that defines a range of the cell to be selected based on the feature amount of the cell to which the selection operation is given up to a first time point. In an image including the cell obtained by image capturing at a second time point later than the first time point, the specifying unit specifies the recommended cell on which the operator is prompted to make a selection based on the parameter.

Disclosed is an optical system using image restoration, including a light source, a pinhole, a testing platform, an image sensor and an image processing device. The pinhole is disposed on a light transmission path of the light source. The testing platform is disposed on the light transmission path of the light source and the pinhole is located between the light source and the testing platform. The testing platform is used to place a testing sample. The image sensor is disposed below the testing platform, and used to sense the testing sample so as to output an optical diffraction signal. The image processing device is electrically connected to the image sensor and used to perform signal processing and optical signal recognition on the optical diffraction signal of the testing sample so as to obtain a clear image of the testing sample.

The present disclosure provides automated robotic microscopy systems that facilitate high throughput and high content analysis of biological samples, such as living cells and/or tissues. In certain aspects, the systems are configured to reduce user intervention relative to existing technologies, and allow for precise return to and re-imaging of the same field (e.g., the same cell) that has been previously imaged. This capability enables experiments and testing of hypotheses that deal with causality over time with greater precision and throughput than conventional microscopy methods.

Reciprocal space map of specific sample locations is generated based on the sample images acquired by irradiating the sample with a charged particle beam at multiple incident angles. The incident angles are obtained by tilting the charged particle beam and/or the sample around two perpendicular axes within the sample plane. The reciprocal space map of a selected sample location is generated based on intensity of pixels corresponding to the location in the sample images.

An image sensor and well structure associated with and extending away from the surface of the image sensor are provided in various apparatuses, methods, and systems for determining the position of a light emitter located in object space. An exemplary method includes (i) providing the image sensor and structure associated therewith, the structure defining a field of view for each pixel within the array of pixels; (ii) determining a light intensity value for photoactivated pixels receiving light from the light emitter; (iii) identifying a first photoactivated pixel having a local maximum of light intensity; (iv) calculating a perpendicular distance between the first photoactivated pixel and the light emitter; and (v) constructing the position of the light emitter based on a position of the first photoactivated pixel in the array of pixels and the perpendicular distance between the first photoactivated pixel and the light emitter.

A cell image analysis method may include: obtaining, for each of cell images, a value of a feature parameter to be used in determination of a type of a cell, by analyzing the cell images; and displaying the value of the feature parameter in association with the each of the cell images.