Abstract: A method for generating an overview image of a sample which is arranged in an observation volume of a microscope by means of a sample carrier is proposed, wherein the sample carrier is illuminated by a first illumination, wherein a preliminary overview image is generated using the first illumination and an overview camera of the microscope, wherein an overview image illumination is chosen on the basis of the preliminary overview image, wherein the sample carrier is illuminated by the overview illumination, and wherein the overview image is generated using the overview image illumination and the overview camera.

Abstract: A method generates an image processing model to calculate a virtually stained image from a microscope image. The image processing model is trained using training data comprising microscope images as input data into the image processing model and target images that are formed via chemically stained images registered locally in relation to the microscope images. The image processing model is trained to calculate virtually stained images from the input microscope images by optimizing an objective function that captures a difference between the virtually stained images and the target images. After a number of training steps, at least one weighting mask is defined using one of the chemically stained images and an associated virtually stained image calculated after the number of training steps. In the weighting mask, one or more image regions are weighted based on differences between locally corresponding image regions in the virtually stained image and in the chemically stained image.

Abstract: A computer-implemented method for generating an image processing model (M) that calculates a virtually stained image (30) from a microscope image (20) comprises a training (15) of the image processing model (M) using training data (T) comprising at least: microscope images (20) as input data into the image processing model (M); target images (50) formed using captured chemically stained images (60); and predefined segmentation masks (70) that discriminate between image regions (71, 72) to be stained and image regions (72) that are not to be stained. The image processing model (M) is trained to calculate virtually stained images (30) from the input microscope images (20) by optimizing a staining reward/loss function (LSTAIN) that captures a difference between the virtually stained images (30) and the target images (50). The predefined segmentation masks (70) are taken into account in the training (15) of the image processing model (M) to compensate errors in the chemically stained images (60).

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: Each optical cable includes an array of at least one optical waveguide and at least one optical ferrule attached to the array of optical waveguides. The housing includes a first housing portion and a second housing portion engaged with the first housing portion. The second housing portion comprises at least one carrier and one frame. The carrier and frame of the second housing portion are configured to support the one or more optical cables. The first housing portion and the second housing portion are configured such that mechanical engagement of the first housing portion with the second housing portion moves the carrier relative to the frame. Movement of the carrier relative to the frame causes a bend in each optical waveguide and rotation of each ferrule. The bend provides a predetermined spring force of the optical waveguides at a predetermined angle of the ferrule.

Abstract: A method generates an image processing model to calculate a virtually stained image from a microscope image. The image processing model is trained using training data comprising microscope images as input data into the image processing model and target images that are formed via chemically stained images registered locally in relation to the microscope images. The image processing model is trained to calculate virtually stained images from the input microscope images by optimizing an objective function that captures a difference between the virtually stained images and the target images. After a number of training steps, at least one weighting mask is defined using one of the chemically stained images and an associated virtually stained image calculated after the number of training steps. In the weighting mask, one or more image regions are weighted based on differences between locally corresponding image regions in the virtually stained image and in the chemically stained image.

Abstract: A method for processing microscope images comprises: acquiring a microscope image captured by a microscope; identifying at least one image section with sensitive information within the microscope image by means of an image processing program which uses provided reference information regarding sensitive information; rendering unrecognizable the at least one identified image section with sensitive information in order to generate an anonymized image; and outputting the anonymized image.

Abstract: A method for determining a distance of a sample reference plane of a sample carrier from a reference plane of a microscope, the microscope including a sample stage for the sample carrier and a camera, comprises the following steps: taking an overview image of the sample carrier by means of the camera; evaluating the overview image and thus detecting at least one characteristic of the sample carrier; ascertaining contextual data of the characteristic from a data set; and determining the distance of the sample reference plane from the reference plane based on the characteristic and the contextual data of the sample carrier. A microscope configured to determine the distance of the sample reference plane of the sample carrier from the reference plane is also described.

Abstract: A microscopy system forms a result image from a microscope image using an ordinal classification model. The ordinal classification model comprises classifiers and is defined by a training designed as follows: predetermined microscope images are input into the ordinal classification model in the training; a target image is given for each predetermined microscope image, wherein binary masks are generated from each target image via a comparison with pixel threshold values; the binary masks are used in the training as classification targets of the classifiers. The training of different classifiers differs in the pixel threshold value that is used to generate the classification targets; In the training, discrepancies between the classification masks and the binary masks are reduced. After the training, each classifier calculates a classification mask for a microscope image to be processed; these classification masks are combined into a result image.

Abstract: In a computer-implemented method for determining a confidence of a calculated classification, a microscope image is processed with an ordinal classification model, which calculates a classification with respect to classes that form an order. The ordinal classification model comprises a plurality of binary classifiers which, instead of calculating classification estimates with respect to the classes, calculate classification estimates with respect to cumulative auxiliary classes, wherein the cumulative auxiliary classes differ in how many consecutive classes of the order are combined. The classification is calculated from the classification estimates of the binary classifiers. A confidence of the classification is determined based on a consistency of the classification estimates of the binary classifiers.

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: Aligning a sample carrier of a microscope includes: capturing an overview image in which the sample carrier and/or a sample carrier environment is at least partially visible; evaluating the overview image with a machine learning model of a computer program, which performs an object localization in the overview image with respect to at least one predefined object; evaluating whether the sample carrier is centred in the overview image with the computer program based on the object localization; if the sample carrier is evaluated as not centred in the overview image: determining a centring movement based on the object location with the computer program, the centring movement indicating a sample stage movement for moving the sample carrier towards an image centre of the overview image; performing a sample stage adjustment based on the centring movement; and capturing a centred overview image after the sample stage adjustment has been performed.

Abstract: At least one image is obtained which images an external view of at least one interchangeable component (111-116) of an optical system (100). Contextual information for the at least one interchangeable component (111-116) is determined on the basis of the at least one image.

Abstract: A method for preparing data for identifying analytes in a sample, one or more analytes being colored with markers in multiple coloring rounds in an experiment, the markers in each case being specific for a certain set of analytes, the multiple markers being detected using a camera, which for each coloring round generates at least one image may contain color information of one or more markers, and the color information of the particular coloring rounds being stored for the evaluation.

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 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 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: 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.