Leica Microsystems Cms Gmbh

Mate Beljan

Kai Ritschel

Christian Schumann

Benjamin Deissler

Wernher Fouquet

Jose Miguel Serra Lleti

Gheorghe Cojoc

José Miguel Serra Lleti

A computer-implemented method for training a denoising model for a microscope includes obtaining a plurality of training images with different image acquisition settings taken with the microscope, the plurality of training images including noise caused by the microscope's hardware, and training the denoising model using the plurality of training images obtained with different image acquisition settings, thereby making the denoising model specific to the microscope's hardware.

Training a denoising model for a microscope

Embodiments of the present invention relate to an optical imaging system, and to methods, systems, and computer programs for such an optical imaging system. The methods comprise obtaining first image data of an imaging device of the imaging system, the first image data comprising a representation of a pattern. The methods comprise obtaining second image data of the pattern from the imaging device after the pattern has been displaced by a stage of the optical imaging system by a distance in a dimension defined relative to the stage. The methods comprise determining an offset between the patterns of the first and second image data in two dimensions. The methods comprise calculating a conversion parameter based on the offset and the distance. A first method comprises controlling a drive unit configured to displace the stage based on the conversion parameter.

Optical imaging system, methods, systems, and computer programs

A method of analyzing a mixed fluorescence response of a plurality of fluorophores in a microscopic sample includes reconstructing individual fluorescence responses from a mixed fluorescence response using spectral un-mixing based on reference emission spectra for fluorophores to be reconstructed, and a procedure for determining and validating reference emission spectra including providing a plurality of image acquisition settings for a sequence of images of the sample equal to, or greater than, the plurality of fluorophores and including an illumination setting for each image, acquiring the sequence of images using the plurality of image acquisition settings and storing each image together with the corresponding illumination setting, determining candidate reference emission spectra for the fluorophores to be reconstructed from the sequence of images of the sample using one or more reference emission spectra determination algorithms, and conditionally using the candidate reference emission spectra as the reference emission spectra in the spectral un-mixing.

Method of analyzing a mixed fluorescence response of a plurality of fluorophores, fluorescence analyzer, fluorescence microscope and computer program

A multispectral microscope system includes a first detector element for capturing a first image of a sample in a first spectral channel, and at least a second detector element for capturing a second image of the sample in a second spectral channel. The first detector element includes a first detector array. The second detector element includes a second detector array different from the first detector array. The microscope system further includes a processor for determining a spatial correlation between the first and second images based on a spectral crosstalk between the first and second spectral channels and registering the first and second images based on the spatial correlation.

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Wetzlar, Germany

Mate Beljan