Abstract: A computer-implemented method for the ordinal classification of at least one microscope image calculates a classification into one of a plurality of classes which form an order with respect to an image property. The microscope image is input into a machine-learned model for ordinal classification. The model comprises binary classifiers which calculate estimates regarding whether the microscope image belongs to cumulative auxiliary classes, which combine different numbers of the classes which follow each other in the order. The estimates can be combined so as to form a total score, wherein the classification occurs by comparing the total score with threshold values which can be defined in a variable manner depending on the application, or interval limits of the classes can be defined so that the classes form intervals of different widths. The model for ordinal classification can also comprise further binary classifiers for inverse auxiliary classes.

Abstract: A microscopy system comprises a microscope configured to capture an overview image and a computing device comprising a model trained for image segmentation, which calculates a segmentation mask based on the overview image. The computing device adjusts a pattern described by a parameterized model to the segmentation mask. An updated segmentation mask is generated using the adjusted pattern.

Abstract: Various examples of the disclosure concern a transfection analysis for cells that are imaged in a microscope image. Techniques are disclosed for the purpose of determining a cell-specific transfection level or a scene-global transfection level using a vector field map.

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 training an image processing system with a machine learning model to perform a virtual multi-angle reconstruction of image stacks recorded with a light-sheet microscope. The method includes: recording at least one light-sheet fine stack comprising multiple image stacks at different illumination angles; determining target outputs from the fine stack, the model, and a classical multi-angle reconstruction; determining learning inputs from a light-sheet coarse stack comprising one or more image stacks at different illumination angles using the model and the classical reconstruction; creating an annotated dataset of the target outputs and learning inputs; and optimizing the model for the virtual multi-angle reconstruction based on the dataset.

Abstract: Processing a microscope image includes forming an input image from a microscope image before the input image is input into an image processing program. The image processing program comprises a learned model for image processing which is trained to calculate image processing results from input training images that show structures with certain image properties. The image processing program calculates an image processing result from the input image. The microscope image is converted into the input image by an image conversion program such that image properties of structures in the microscope image become closer to the image properties of the structures in the input training images.

Abstract: A method generates an overall image of a sample, wherein the method includes providing at least two image recordings of the sample; providing a respective coefficient array for the at least two image recordings; combining the at least two image recordings to form a combined image of the sample, wherein the contributions of the image portions of the respective image recording to the combined image are determined by the respective coefficient array; modifying at least one coefficient array of the coefficient arrays to improve the quality of the combined image; combining the at least two image recordings to form a new combined image of the sample on the basis of the modified coefficient arrays; and outputting the new combined image as overall image or forming the overall image on the basis of the modified coefficient arrays from at least two image recordings of the sample and outputting the overall image.

Abstract: In a method for processing microscope images, at least a first image data set (30) of a microscope (10) is received. At least a first generative model (40) that describes the first image data set (30) is estimated with a first computing device (20) based on the first image data set (30). Either a first generated image data set (50) is generated by the first generative model (40) and transmitted to a data exploitation device (60), or the first generative model (40) is transmitted to a data exploitation device (60) and subsequently a first generated image data set (50) is generated by means of the first generative model (40). Generated image data of the first generated image data set (50) is entirely data generated from the first generative model (40) and does not comprise processed image data of the first image data set (30) captured by the microscope (10). The first generated image data set (50) is then exploited by means of the data exploitation device (60).

Abstract: In a method for checking the rotation of a microscope camera, an overview image is captured with an overview camera of a microscope, wherein a rotational orientation of the overview camera relative to a sample stage of the microscope is known. In addition, a microscope image is captured with a microscope camera of the microscope. A rotational orientation of the microscope camera relative to the sample stage is calculated based on a relative rotation between the microscope image and the overview image, wherein the relative rotation is established by means of image structures in the microscope image and the overview image and using the known rotational orientation of the overview camera relative to the sample stage.

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 computer-implemented method for instance segmentation of at least one microscope image showing a plurality of objects, comprising: calculating positions of object centers of the objects in the microscope image; determining which image areas of the microscope image are covered by the objects; calculating Voronoi regions using the object centers as Voronoi sites; and determining an instance segmentation mask by separating the image areas covered by the objects into different instances using boundaries of the Voronoi regions.

Abstract: A microscopy system comprises a microscope configured to capture an overview image and a computing device comprising a model trained for image segmentation, which calculates a segmentation mask based on the overview image. The computing device adjusts a pattern described by a parameterized model to the segmentation mask. An updated segmentation mask is generated using the adjusted pattern.

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: Techniques for controlling a microscopy system and for processing microscope images are disclosed. In this context, a user input in free-text format is processed in order to create a prompt for a machine-learned text-to-text foundation model. The output of the foundation model can be used subsequently to solve an image processing task or for controlling the microscopy system.

Abstract: A number of techniques for assessing simulation models for use in microscopy are provided. In one example technique, a first image (IA) of a sample is recorded with a first image recording type, and storing image values of the first image (IA) are stored. Based on a simulation model (SMA?C) being applied to the first image (IA), a simulated image (IA?C) of a third image recording type of the sample is simulated. A third image (IC) of the sample is recorded with a third image recording type. The third image (IC) is compared with the simulated image I(A?C) of the third image recording type for verification of compliance with previously defined quality criteria, and the simulation model (SMA?C) is classified as permissible when the quality criteria are complied with.

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: Various examples of the disclosure relate to techniques to count cells in a microscopy image and/or to determine a degree of confluence of the cells in the microscopy image. To that end, machine-learned algorithms are used.

Abstract: Processing a microscope image includes forming an input image from a microscope image before the input image is input into an image processing program. The image processing program comprises a learned model for image processing which is trained with training images that show structures with certain image properties. The image processing program calculates an image processing result from the input image. The microscope image is converted into the input image by an image conversion program in such a manner that image properties of structures in the input image are modified with respect to image properties of the structures of the microscope image so that they are closer to the image properties of the structures of the training images.

Abstract: A method, device, and computer program product are designed for non-convolutional image processing in microscopy of an input image into an output image using an artificial neural network with at least one contracting path including layers, at least one expanding path including layers, and at least one filter kernel. The method includes determining, in one or multiple artificial neural network layers, a similarity metric between at least one filter kernel and one output of the previous layer. Additionally, in at least one layer of the contracting path, the resolution of the output of the previous layer is reduced, and, in at least one layer of the expanding path, the resolution of the output of the previous layer is increased. The first artificial neural network layer treats the input image as the output of the previous layer, and the output of the last artificial neural network layer is the output image.

Abstract: A method for repositioning a focus position of an imaging device in a target focus position in a sample in an experiment comprises: defining the target focus position, repositioning a current focus position based on the target focus position, comprising determining one or more compare signatures based on a current focus position, determining one or more distances in each case between the compare signatures and a target signature based on the target focus position, adapting the current focus position based on the distances. A signature is an output of a machine learning model corresponding to a focus position and based on an image of the sample recorded with the focus position, and the target focus position is a focus position in the sample in which the imaging device captures a target image of the sample and the machine learning model outputs the target signature when the target image is input.