Carl Zeiss Microscopy Gmbh

Daniel Haase

Manuel Amthor

Thomas Ohrt

Markus Sticker

Alexander Freytag

Michael Gögler

Christian Kungel

Ralf Wolleschensky

Thomas Kalkbrenner

Ingo Kleppe

Daniel Schwedt

Tiemo Anhut

Jörg Siebenmorgen

Tobias Kaufhold

Christian Dietrich

Ralf Engelmann

Frank Klemm

Dominik Stehr

Burkhard Roscher

Matthias Eibl

Anselm Brachmann

Sebastian Backs

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

Assessment method for simulation models in microscopy

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.

Microscopy system and method for determining an orientation of a sample carrier

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.

Microscopy system and method for processing a microscope image

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.

Method and device for non-convolutional image processing

In a method for evaluating image processing results, at least two microscope images that show the same structure are received. Each of the microscope images is processed by means of an image processing algorithm for calculating a respective image processing result. At least one confidence score of the image processing results is determined based on a degree of correspondence between the image processing results calculated from different microscope images.

Microscopy system and method for evaluating image processing results

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.

Machine-learned cell counting or cell confluence for a plurality of cell types

A microscopy system for generating training data for a machine learning model comprises a microscope configured to capture an image. The microscopy system further comprises a computing device configured to generate a segmentation mask based on the image, adjust a pattern described by a parameterized model to the segmentation mask, generate an updated segmentation mask using the adjusted pattern, and incorporate the updated segmentation mask or an image derived from the same in the training data.

Microscopy system and method for generating training data

A method can be used for sensing and processing image data for an object to be imaged. The object is scanned incompletely by virtue of regions (eB) of the object being sensed, where the sensed image regions (eB) alternate with non-sensed image regions (neB) of the object. Image data (rBD) of the non-sensed image regions (neB) are reconstructed on the basis of the sensed image data (eBD) of the sensed image regions (eB). A noise signal (N) of the sensed image data (eBD) of the sensed regions (eB) is ascertained and transferred to the reconstructed image data (rBD) of the non-sensed regions (neB), so that a user obtains a homogeneous visual impression in relation to the noise arising in the overall image data of the object visualized in a resultant overall image (rGB).

Method for compressed sensing and processing of image data

A method, a device, and a computer program product captures microscopy objects in image data that includes first images recorded with a first contrast and second images recorded with a second contrast, wherein in each case, one of the first and one of the second images can be correspondingly assigned to each other. The method includes capturing information indicating microscopy objects in at least one of the second images, transferring the captured information to those of the first images which correspond to the at least one of the second images, and capturing information indicating microscopy objects in the first images, to which the captured information of the second images was transferred by using the transferred information.

Method and device for capturing microscopy objects in image data

In a computer-implemented method for controlling a microscope, a textual input describing a desired microscope image and an employed sample is received. The textual input and an overview image of the employed sample are input into a large language model, which is trained to process the textual input and the overview image together to calculate microscope settings for capturing a microscope image that corresponds to the desired microscope image. A microscope image is then captured with these calculated microscope settings.

Microscopy system and associated methods

System for state monitoring of a microscope the system having at least one measuring sensor in each case for capturing at least one time-variable chemical and/or physical quantity, a camera for recording an image in a field of view and a processing unit. The at least one measuring sensor has a display area and displays thereon a measured value for the captured time-variable chemical and/or physical quantity. The camera is arranged so that the display areas of at least one measuring sensor are located in the field of view and the processing unit is configured to evaluate the image and to extract the display areas contained in the image therefrom. Also, a method for state monitoring of a microscope is disclosed, wherein at least one measuring sensor with a display area is provided in order to capture in each case at least one time-variable chemical and/or physical quantity, and an image is recorded. The image is recorded so that it contains the display areas of at least one measuring sensor. The display areas are identified in the image, the image is evaluated and the measured values contained in the image are extracted.

System and method for monitoring of states of components of a microscope

A method for preparing a microscope for imaging a sample. The method includes the following steps: providing the microscope for imaging the sample, wherein the microscope comprises an objective having a motor-adjustable objective correction ring for correcting imaging aberrations; acquiring sample information comprising at least one of the following indications: thickness of a cover glass, material of the cover glass, sample temperature, sample type, sample location on a sample carrier, embedding medium of the sample, or immersion medium; imaging the sample using the microscope for generating at least one raw image of the sample with a first setting of the objective correction ring; and inputting the at least one raw image and the sample information into a machine algorithm and determining a second setting of the objective correction ring, which reduces the imaging aberrations vis à vis the first setting, by means of the algorithm on the basis of the raw image and the sample information.

Method of preparing a microscope for imaging a sample

A computer-implemented method comprises the following steps. In one step an image is acquired which is captured in the context of a microscopy measurement and images a sample to be examined. In one step the microscopy measurement is monitored in an automated manner. On the basis of the automated monitoring of the microscopy measurement, one or more labels are created, wherein said one or more labels comprise semantic context information of the microscopy measurement. On the basis of the image as input and said one or more labels as ground truth, a machine-learned algorithm is trained which provides semantic context information on the basis of images captured in the context of microscopy measurements. In a further step a further image is acquired, which is captured in the context of the microscopy measurement or a further microscopy measurement by the microscope and images the sample or a further sample. In a further step the trained machine-learned algorithm is applied to the further image in order to predict further semantic context information for the further image.

Automated training of a machine-learned algorithm on the basis of the monitoring of a microscopy measurement

A microscopy system comprises a microscope configured to capture raw data and a computing device configured to derive at least one microscope image from the raw data and to run a processing program for processing the microscope image. The computing device is configured to first supply a microscope image which is to be processed with the processing program to a checking program, which checks the supplied microscope image with respect to an evaluation criterion predefined for the processing program. The microscope image is only transmitted to the processing program in the event of a positive checking result.

Microscopy system and method for checking input data

A microscopy system comprises a microscope for analyzing a sample, a computing device for processing measurement signals and at least one microphone for capturing sounds. The computing device is configured to evaluate captured sounds in order to identify a microscope activity in progress or command an intervention in the microscope activity in progress or identify ambient sounds based on microscope sounds.

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Zoellnitz, Germany

Daniel Haase