Abstract: A method is proposed for detecting at least one fluorescence pattern on an immunofluorescence image of a biological cell substrate, comprising the following steps: incubating the cell substrate with a liquid patient sample, which potentially includes primary antibodies, and furthermore with secondary antibodies, which are marked using a fluorescence stain, irradiating the cell substrate using excitation radiation and capturing the immunofluorescence image, determining segmentation information comprising at least one first and one second segmentation area, wherein the segmentation areas each represent a respective cell substrate area, via segmentation of the immunofluorescence image using a first neural network, determining a boundary area, which represents a transition from the first cell substrate area towards the second cell substrate area in the fluorescence image, on the basis of the segmentation information, selecting multiple partial images from the immunofluorescence image along the boundary area, dete

Abstract: A method is proposed for detecting a presence of a fluorescence pattern on an immunofluorescence image of a biological cell substrate, comprising the following steps: incubating the cell substrate with a liquid patient sample, which potentially comprises primary antibodies, and furthermore with secondary antibodies, which are marked using a fluorescence stain, irradiating the cell substrate using excitation radiation and capturing the immunofluorescence image, determining respective items of location information, which indicate respective locations of respective relevant subsections of the cell substrate in the fluorescence image, and determining respective first partial confidence measures of respective presences of the fluorescence pattern on the respective subsections by means of a first neural network on the basis of the overall fluorescence image, extracting respective image subsections, which correspond to the respective subsections of the cell substrate, from the fluorescence image on the basis of the

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 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: 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: 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.