Abstract: A method for preparing data for identifying analytes in a sample, in which in an experiment one or more analytes are colored with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting the multiple markers using a camera, which for each coloring round generates at least one image containing multiple pixels and color values assigned thereto, the image including colored signals and uncolored signals, wherein a colored signal is a pixel having a color value that originates from a marker, and an uncolored signal is a pixel having a color value that is not based on a marker, and storing the color information of the particular coloring rounds for evaluating the color information.

Abstract: A method for assigning image areas of an image series to result classes by means of a processing model that was trained to assign image areas from the image series to a result class. The image series is generated by marking analytes with markers in a plurality of coloring rounds and detecting the markers with a camera. The camera captures or acquires an image of the image series in each coloring round, and the markers are selected in such a way that the signal series of analytes in an image area across the image series include colored signals and uncolored signals. The colored and uncolored signals of the analyte signal series have at least one particular ratio of one of the colored and/or uncolored signals of the respective signal series.

Abstract: A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image containing multiple pixels and color values assigned thereto, which may contain color information of one or more markers, and storing the color information of the particular coloring rounds for evaluating the color information, a data point in each case including one or more contiguous pixels in the images of the multiple coloring rounds that are assigned to the same location in a sample.

Abstract: A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that includes multiple pixels and that may contain color information of one or more markers, and storing the images of the particular coloring rounds stored for evaluating the color information, wherein the color values determined in the individual coloring rounds are clustered, according to their intensity values, in local or global clusters with similar intensity values, and only the clustered data are stored.

Abstract: A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that contains multiple pixels and includes colored signals and uncolored signals, a colored signal being a pixel containing color information of a marker, and an uncolored signal being a pixel containing color information that is not based on a marker, and storing the images of the particular coloring rounds for evaluating the color information, a data point in each case including one or more contiguous pixels in the images of the multiple coloring rounds, which are assigned to the same location in a sample, wherein each of the data points is assessed, based on the color information of at least the present image, for whether it may be a candidate data point, i.e.

Abstract: A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that includes multiple pixels to which a color value is assigned in each case as color information, and includes colored signals and uncolored signals, wherein a colored signal is a pixel containing color information of a marker, and an uncolored signal is a pixel containing color information that is not based on a marker. A data point in each case includes one or more contiguous pixels in the images of the multiple coloring rounds that are assigned to the same location in a sample.

Abstract: A method for identifying analytes in an image series, the image series being generated by marking the analytes with markers in multiple coloring rounds and detecting the markers using a camera. The markers are selected in such a way that image signals of an analyte in an image area over the image series include colored signals and uncolored signals. The method comprises extracting multiple signal series of an image area of the image series in each case and filtering out candidate signal series from the extracted signal series. A ratio of at least one of the colored and/or uncolored signals of a candidate signal series to at least one other of the colored and/or uncolored signals of the particular signal series is a characteristic ratio, and/or a candidate signal series has a characteristic signature that has at least one characteristic ratio.

Abstract: Method for determining a signal composition of signal series of an image series with an analyte data evaluation system, wherein the image series is generated by marking analytes with markers in a plurality of coloring rounds and detecting the markers with a camera, the camera acquires an image of the image series in each coloring round, the markers being selected in such a way that the signal series of analytes in an image area across the image series comprise colored and uncolored signals, and that the signal series of different analyte types each have a specific sequence of colored signals and uncolored signals, and the different analyte types can be identified based on the specific sequences, comprising: receiving the signal series; importing a codebook, wherein the codebook comprises a target series for all signal components and the target series comprise analyte target series.

Abstract: A microscopy system comprises a microscope and a computing device which is configured to control the microscope to capture a plurality of raw overview images showing the sample environment with different capture parameters. The computing device comprises a machine learning model trained with training raw overview images, which receives the raw overview images as input and generates therefrom an output which is or which defines the overview image.

Abstract: A microscopy system comprises a microscope for image capture and a computing device. An image processing model is trained to calculate an image processing result from a microscope image. A quality testing program makes a quality statement regarding a quality of the image processing model from learned model parameter values of the image processing model.

Abstract: A machine-learned model for processing microscope data is trained using a dataset containing microscope data. An embedding of the dataset in a feature space is calculated. The embedding is analyzed in order to determine training design specifications for a training of the model. The training is defined as a function of the training design specifications and subsequently implemented, whereby the model is configured to calculate a processing result from microscope data to be processed.

Abstract: A computer-implemented method tests a sensitivity of an image processing model trained using training data which includes microscope images. The training data is also used to form a generative model that can produce a generated microscope image from an input parameter set. The generative model is used to produce a series of generated microscope images by varying at least one parameter of the parameter set. An image processing result is calculated from each of the generated microscope images using the image processing model. A sensitivity of the image processing model to the at least one parameter is then ascertained based on differences between the image processing results.

Abstract: The invention relates to a method and device for generating an overview contrast image of a sample carrier in a microscope, which images a sample arranged on the sample carrier. The sample carrier on which the sample is located is illuminated in transmitted light from a two-dimensional illumination array, which has individually switchable single light sources and illuminates the sample volume in transmitted light, and a processing unit, which activates and reads out the camera in order to record multiple different raw overview images of the sample carrier.

Abstract: For the color correction of microscope images, an object that corresponds to a predetermined object type of a known color is localized in a microscope image using an image processing program. Based on an image region of the localized object, a color correction is determined with which a color of the localized object in the image region is brought into accordance with the known color. The color correction is applied to at least one section of the microscope image or of another microscope image or is used in a capture of a further microscope image.

Abstract: A microscopy system comprises a microscope which can record at least one microscope image and a computing device which comprises a trained image processing model set up to calculate an image processing result based on the at least one microscope image. A method for verifying a trained image processing model, which can be performed by the computing device, can include receiving a validation image and an associated target image; entering the validation image into the trained image processing model, which calculates an output image therefrom; entering image data based on at least the output image and the associated target image into a trained verification model which is trained to calculate an evaluation that indicates a quality that depends on the image data for entered image data; and calculating an evaluation by the trained verification model based on the entered image data.

Abstract: An overview image of a sample carrier is obtained in a method for ascertaining a measurement location of a microscope. A mapping or homography is determined, by means of which the overview image is perspectively convertible into a plan view image on the basis of a height of the sample carrier. The measurement location is identified, with the aid of the determined mapping/homography, in the overview image or in an output image derived therefrom.

Abstract: A microscopy system for generating an HDR image comprises a microscope for capturing a plurality of raw images that show a scene with different image brightnesses and a computing device configured to generate the HDR image by combining at least two of the raw images. The computing device is also configured to set coefficients for different regions in the raw images depending on objects depicted in those regions, wherein it is defined via the coefficients whether and to what extent pixels of the raw images are incorporated in the HDR image. The computing device is further configured to generate the HDR image by combining the raw images based on the set coefficients. A method generates an HDR image by combining raw images based on coefficients that are set depending on objects depicted in the raw images.

Abstract: In a computer-implemented method for determining an orientation of a sample carrier of a microscope, an overview image showing at least a part of a sample carrier with a plurality of sample regions is received. The overview image is evaluated in order to localize predetermined structures. At least one image region that shows at least one predetermined structure is analyzed in order to calculate an orientation indication that discriminates between orientations of the sample carrier that are rotated by 180° relative to each other.

Abstract: A distance determination system for a microscope system for coarse focus setting includes a sample stage with a placement surface for holding a displaceable sample carrier, an overview camera with a non-telecentric objective for producing digital images, directed at the sample stage, and an evaluation unit, which includes a storage system storing at least two recorded digital images of the sample stage at different viewing angles, a trained machine-learning-based system for identifying corresponding structures of a sample carrier in the sample stage in the two recorded digital images and a distance determination unit, which determines the distance of a reference point of the sample carrier from a reference point of the overview camera based on the different viewing angles onto the sample stage, a pixel distance of the two recorded digital images with respect to one another using the associated corresponding structures contained in the recorded images.

Abstract: In a computer-implemented method for editing overview images of a microscope, a raw overview image is obtained in a first step. The raw overview image is transformed by means of calibration data into an overview image in which image directions coincide with given directions. At least one geometric property of text of at least one text region in the overview image is determined. A transformed text region is calculated taking into account the determined geometric property so that the transformed text region exhibits a desired geometric property of the text.