Carl Zeiss Microscopy Gmbh

Daniel Haase

Manuel Amthor

Thomas Ohrt

Markus Sticker

Alexander Freytag

Michael Gögler

Christian Kungel

Ralf Wolleschensky

Thomas Kalkbrenner

Ingo Kleppe

Tiemo Anhut

Daniel Schwedt

Jörg Siebenmorgen

Tobias Kaufhold

Christian Dietrich

Ralf Engelmann

Frank Klemm

Dominik Stehr

Burkhard Roscher

Matthias Eibl

Anselm Brachmann

Sebastian Backs

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.

Microscopy system and method for monitoring microscope activity

A microscopy system comprises a microscope with an overview camera for capturing at least one overview image of a sample carrier designed to receive at least one sample fluid; and a computing device configured to determine at least one sample image area of the at least one sample fluid within the at least one overview image. The computing device comprises an evaluation program into which a determined sample image area is entered and which is configured to determine a fluid state of the associated sample fluid based on the sample image area.

Microscopy system and method for analyzing an overview image

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.

Microscopy system and method for the color correction of microscope images

A method for light sheet microscope examination of a specimen, the specimen being illuminated with a light sheet by an illumination objective, and a light sheet microscope for performing the method. Light emitted by the specimen is imaged onto an area-type detector by a detection objective. An optical axis of the detection objective encloses with an optical axis of the illumination objective an angle that differs from 0° and from 180° and intersects the light sheet in a light sheet plane. The area-type detector records an image. A neural network analyzes the image to determine whether the light sheet plane is located in a focal plane of the detection objective, and/or in which direction along the optical axis the light sheet plane is located from the focal plane, and/or at what distance, measured along the optical axis of the detection objective, the light sheet plane is from the focal plane.

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Zoellnitz, Germany

Daniel Haase