Embodiments relate to systems and methods for sample image capture using integrated control. A digital microscope or other imaging device can be associated with a sample chamber containing cell, tissue, or other sample material. The chamber can be configured to operate using a variety of environmental variables, including gas concentration, temperature, humidity, and others. The imaging device can be configured to operate using a variety of imaging variables, including magnification, focal length, illumination, and others. A central system control module can be used to configure the settings of those hardware elements, as well as others, to set up and carry out an image capture event. The system control module can be operated to control the physical, optical, chemical, and/or other parameters of the overall imaging environment from one central control point. The variables used to produce the image capture can be configured to dynamically variable during the media capture event.

The disclosure provides a method of generating an artificial fluorescent image of cells is provided. The method includes receiving a brightfield image generated by a brightfield microscopy imaging modality of at least a portion of cells included in a specimen, applying, to the brightfield image, at least one trained model, the trained model being trained to generate the artificial fluorescent image based on the brightfield image, receiving the artificial fluorescent image from the trained model

A method for matching a three-dimensional first image of at least one discrete entity with a three-dimensional second image of the at least one discrete entity is provided. The at least one discrete entity includes a biological sample and a plurality of constituent parts of a marker. The method includes: generating a first representation of the marker from the first image; generating a second representation of the marker from the second image; and based upon the representations matching, matching the first image with the second image; or based upon the representations not matching, rejecting the match. Generating the representations includes determining vectors from at least one reference item to at least some of the constituent parts of the marker, determining for the vectors at least one value of a property, and generating the representations of the marker based on a frequency of the at least one value of the property.

Examples relate to a microscope system comprising a Light-Emitting Diode (LED)-based illumination system and at least one image sensor assembly, and to a corresponding system, method and computer program. The LED-based illumination system is configured to emit radiation power having at least one peak at a wavelength that is tuned to an excitation wavelength of at least one fluorescent material and/or to emit radiation power across a white light spectrum, with the light emitted across the white light spectrum being filtered such that light having a wavelength spectrum that coincides with at least one fluorescence emission wavelength spectrum of the at least one fluorescent material is attenuated or blocked. The at least one image sensor assembly is configured to generate image data, with the image data (at least) representing light reflected by a sample that is illuminated by the LED-based illumination system. The microscope system comprises one or more processors, configured to process the image data to generate processed image data.

A method for analyzing 2D material thin film and a system for analyzing 2D material thin film are disclosed. The detection method includes the following steps: capturing sample images of 2D material thin films; measuring the 2D material thin films by a Raman spectrometer; performing a visible light hyperspectral algorithm on the sample images by a processor to generate a plurality of visible light hyperspectral images; performing a training and validation procedure, performing an image feature algorithm on the visible light hyperspectral images, and establishing a thin film prediction model based on a validation; and capturing a thin-film image to be measured by the optical microscope, performing the visible light hyperspectral algorithm, and then generating a distribution result of the thin-film image to be measured according to an analysis of the thin film prediction model.

An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

The present invention relates to a method of reading out information from a data carrier and to a data carrier utilizing the concept of structured-illumination microscopy or saturated structured-illumination microscopy.

A microscope control unit includes at least one connection which is connectable to one or more electrically addressable microscope components or other electrically controllable components. At least one connection parameter of the at least one connection is programmatically configurable. At least one script with script commands is provided on the microscope control unit. At least one terminal is addressable by the script commands.

A method is provided comprising: positioning a slide that includes a concave region that is formed in a top surface of the slide and that can contain an object, such that a focal point of an objective lens of a fluorescence lifetime imaging microscopy (FLIM) device is within an area of the concave region between a bottom surface of the concave region and the top surface of the slide; and using the FLIM device to capture a sequence of wide field images of a portion of the object within a field of view of the objective lens.

A microscopy method is for producing an electronic image of an object, wherein the object is imaged with an adjustable optical imaging scale on an image detector. The method includes: selecting a parameter for the electronic image, wherein the parameter can be influenced by the optical imaging scale and differs from the image field dimensions, and setting a setpoint value range for the parameter, setting a total imaging scale for the electronic image, wherein adjusting or controlling the parameter of the electronic image is implemented such that, at the same time, the parameter of the electronic image lies in the specified setpoint value range with a tolerance and the set total imaging scale is obtained, wherein the optical imaging scale forms a basis for a manipulated variable of the adjustment or closed-loop control and a digital image magnification is carried out on the basis of the set total imaging scale.