Euroimmun Medizinische Labordiagnostika Ag

Joern Voigt

Stefan Gerlach

Christian Marzahl

Christopher Krause

Melanie Hahn

Jens Krauth

Maick Danckwardt

Jens Hocke

Christine Kroeger

A method for detecting a potential presence of a fluorescence pattern type on an organ section via immunofluorescence microscopy and digital image processing, the method comprising: providing an organ section on a slide, incubating the section with a liquid patient sample potentially including primary antibodies and secondary antibodies labelled with a fluorescent dye, acquiring a fluorescence image of the organ section, determining, by segmentation of the fluorescence image via a first neural network, a sub-area of the fluorescence image relevant to formation of the fluorescence pattern type, determining, via a second neural network, the measure of confidence indicating an actual presence of the fluorescence pattern type, determining, validity information indicating a degree of a validity of the measure of confidence, and outputting the measure of confidence of the actual presence of the fluorescence pattern type and of the validity information.

Method and apparatus for detecting a presence of a fluorescence pattern type on an organ section by means of immunofluorescence microscopy

A method and apparatus are provided for detecting respective potential presences of respective different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells (HEp cells), wherein the fluorescence pattern types include different antinuclear antibody fluorescence pattern types. A method is also provided for detecting potential presences of different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells by means of digital image processing, as well as a computing unit, a data network device, a computer program product and a data carrier signal therefor.

Method of detecting presences of different antinuclear antibody fluorescence pattern types without counterstaining and apparatus therefor

An image processing method is provided for displaying cells from a plurality of pathology images or overall images. A respective overall image represents a respective patient tissue sample or a respective patient cell sample. The method includes the steps of: providing the overall images, detecting individual cell images in the overall images by means of a computer-assisted algorithm, determining classification data by means of the computer-assisted algorithm, wherein the classification data indicate a respective unique mapping of a respective detected cell image to one of a plurality of cell classes, and wherein the classification data further have a respective measure of confidence in respect of the respective unique mapping, generating respective class images for the respective cell classes, wherein a class image of a cell class reproduces the cell images mapped to the cell class in a regular arrangement and with a predetermined order, and wherein further the order of the mapped cell images is chosen on the basis of the measures of confidence of the mapped cell images, and further, displaying a portion of at least one class image.

Image processing method for displaying cells of a plurality of overall images

A method and a device are useful for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid (DNA) usingcells by fluorescence microscopy and by digital image processing.

Method for detecting a binding of antibodies from a patient sample to double-stranded DNA using Crithidia luciliae cells and fluorescence microscopy

A method is for detecting respective potential presence of respective different antinuclear antibody fluorescence pattern types on a biological cell substrate including human epithelioma cells, including: acquiring a first image which represents staining of the cell substrate by a first fluorescent dye, and acquiring a second image which represents staining of the cell substrate by a second fluorescent dye, detecting, on the basis of the first image, respective image segments which in each case represent at least one mitotic cell, selecting, on the basis of the detected image segments, subimages of the first image and subimages corresponding thereto of the second image and detecting, on the basis of the selected subimages of the first image and of the selected subimages of the second image, respective actual presence of respective cellular fluorescence pattern types by means of a convolutional neural network.

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Luebeck, Germany

Joern Voigt