Abstract: 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.

Abstract: There is proposed a method for detecting a presence of a fluorescence pattern type on an organ segment via immunofluorescence microscopy and digital image processing. The steps comprise: provision of the organ segment, incubation of the organ segment with a liquid patient sample, incubation of the organ segment with secondary antibodies which have been labelled with a fluorescent dye, acquisition of a fluorescence image of the organ segment in a colour channel corresponding to the fluorescent dye, and provision of the fluorescence image to a neural network. What takes place by means of the neural network is simultaneous determination of segmentation information through segmentation of the fluorescence image and, furthermore, of a measure of confidence indicating an actual presence of the fluorescence pattern type.

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

Abstract: A method for detecting at least one potential presence of at least one fluorescence pattern type on an organ section by means of immunofluorescence microscopy and by means of digital image processing is disclosed. An organ section is provided on a slide and the section is incubated with a liquid patient sample which potentially includes primary antibodies and with secondary antibodies which have been labelled with a fluorescent dye. A fluorescence image of the organ section is acquired. A determination is made, by segmentation of the fluorescence image by means of a first neural network, of a sub-area of the fluorescence image that is relevant to formation of the fluorescence pattern type. A determination is made, on the basis of the fluorescence image by means of a second neural network, of the measure of confidence that indicates an actual presence of the fluorescence pattern type.

Abstract: 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.

Abstract: A method for detecting at least one potential presence of at least one fluorescence pattern type on an organ section by means of immunofluorescence microscopy and by means of digital image processing is disclosed. An organ section is provided on a slide and the section is incubated with a liquid patient sample which potentially includes primary antibodies and with secondary antibodies which have been labelled with a fluorescent dye. A fluorescence image of the organ section is acquired. A determination is made, by segmentation of the fluorescence image by means of a first neural network, of a sub-area of the fluorescence image that is relevant to formation of the fluorescence pattern type. A determination is made, on the basis of the fluorescence image by means of a second neural network, of the measure of confidence that indicates an actual presence of the fluorescence pattern type.

Abstract: 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.

Abstract: A method is useful for automated detection of antibodies in a liquid biological sample with an antigen chip having antigen spots which are applied thereon and which have an identical, common dye. The antigen spots form respective antigen spot sets which form corresponding respective, regular antigen spot patterns. Furthermore, reference spots comprising the identical dye are applied, and form a reference spot set which forms a regular reference pattern. The reference pattern differs with respect to its regularity from the antigen spot patterns. Through a first image information item which represents a color of the reference spots and of the antigen spots due to the identical, first dye and through a second image information item which represents a potential color of the antigen spots due to a second dye after an incubation, binding of antibodies of the biological sample to respective antigen types is then determined by image processing.

Abstract: There is proposed a method for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid using Crithidia luciliae cells by means of fluorescence microscopy, including the steps of: provision of a substrate which has multiple Crithidia luciliae cells, incubation of the substrate with the patient sample which potentially has the autoantibodies, incubation of the substrate with secondary antibodies which have each been labelled with a preferably green fluorescent dye, acquisition of a fluorescence image of the substrate, identification by means of a first pretrained convolutional neural network of respective sub-images in the one fluorescence image that each represent a Crithidia luciliae cell, furthermore respective processing of at least one subset of the respective sub-images by means of a second pretrained convolutional neural network for determining respective binding measures which indicate a respective extent of a binding of autoantibodies in a respective kinetop

Abstract: 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.

Abstract: A method is useful for automated detection of antibodies in a liquid biological sample with an antigen chip having antigen spots which are applied thereon and which have an identical, common dye. The antigen spots form respective antigen spot sets which form corresponding respective, regular antigen spot patterns. Furthermore, reference spots comprising the identical dye are applied, and form a reference spot set which forms a regular reference pattern. The reference pattern differs with respect to its regularity from the antigen spot patterns. Through a first image information item which represents a color of the reference spots and of the antigen spots due to the identical, first dye and through a second image information item which represents a potential color of the antigen spots due to a second dye after an incubation, binding of antibodies of the biological sample to respective antigen types is then determined by image processing.

Abstract: 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.

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

Abstract: An antigen chip is described, the latter comprising a plane substrate surface; and antigen spots, which are applied in a predetermined pattern on the substrate surface and which contain a first fluorescence dye. Further, an apparatus and a method for detecting antibodies in a sample are provided. Even further, the application relates to kit for use in a method for detecting antibodies in a sample.