Carl Zeiss Microscopy GmbH

Provided is a computer-implemented method for generating a second recording of a sample in a predetermined target contrast type, the method comprising detecting at least one structure using a segmentation algorithm, wherein the structure is contained in a first recording of the sample in a predetermined first input contrast type and should be visible in the second recording of the sample in the target contrast type, and generating the second recording of the sample in the target contrast type, the generation of the second recording in the target contrast type including determining first image intensity values of the first recording of the sample to generate the second recording based on the determined first image intensity values and the at least one structure detected in the first recording, wherein the predetermined target contrast corresponds to a microscopy contrast type.

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 Inv ).

In a computer-implemented method for modifying microscope images, a generative model is trained using a training dataset which comprises a plurality of microscope images. After the training, the generative model is configured to compute a generated microscope image from a feature vector derived from a feature space. It is established which image properties are affected by which feature variables in the feature space. A microscope image to be modified is received and projected into the feature space in order to obtain an associated feature vector. One or more feature variables of the feature vector are modified in order to change one or more image properties, whereby a modified feature vector is generated. The modified feature vector is projected back into an image space by inputting the modified feature vector into the generative model, thereby generating a modified microscope image.

In a method, a device and a computer program product for acquiring images for training data to train a statistical model by machine learning for image processing in microscopy, the training data is made up of pairs of input images and output images from image processing. The method includes acquiring at least one image, analyzing the at least one image according to predetermined criteria, determining acquisition parameters for the acquisition of output images on the basis of the analysis results, and acquiring output images on the basis of the determined acquisition parameters.

A method for processing microscope images in order to generate an image processing result comprises: implementing a convolutional neural network, wherein a first convolutional layer calculates an output tensor from an input tensor formed from a microscope image. The output tensor is input into one or more further layers of the convolutional neural network in order to calculate the image processing result. The first convolutional layer comprises a plurality of filter kernels. At least several of the filter kernels are respectively representable by at least one filter matrix with learning parameters and dependent filter matrices with implicit parameters, which are determined by means of the learning parameters and one or more weights to be learned, wherein the filter matrices with learning parameters of different filter kernels are different from one another and different layers of the output tensor are calculated by different filter kernels.

Images are captured with a sample object in various arrangements relative to lighting and a detector. The images are then combined image point by image point on the basis of a comparison of image point values of image points of said images. This achieves a reduction in interference, i.e. reflections and/or shadows can be reduced.

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 Inv ).

In a method for reducing an image vignetting, a microscope image is captured by means of a microscope, wherein the microscope image exhibits a vignetting. At least the microscope image is input into an image processing model which has been trained, by means of training data comprising input images with vignetting and associated target images without vignetting, to calculate a vignetting-free microscope image from at least the input microscope image. The method further comprises the outputting of a calculated vignetting-free microscope image by the image processing model.

In a computer-implemented method for modifying microscope images, a generative model is trained using a training dataset which comprises a plurality of microscope images. After the training, the generative model is configured to compute a generated microscope image from a feature vector derived from a feature space. It is established which image properties are affected by which feature variables in the feature space. A microscope image to be modified is received and projected into the feature space in order to obtain an associated feature vector. One or more feature variables of the feature vector are modified in order to change one or more image properties, whereby a modified feature vector is generated. The modified feature vector is projected back into an image space by inputting the modified feature vector into the generative model, thereby generating a modified microscope image.

A description is given of a method for increasing resolution in microscopy, comprising providing at least one recorded sample image ( 22 ) which was generated by means of a microscope ( 2 ), providing a point spread function which characterizes an imaging behaviour of the microscope ( 2 ), and calculating a sample image with increased resolution from the recorded sample image ( 22 ), wherein the calculating is effected in an iteration process (S 4 ) which repeatedly passes through an iteration loop (S 4 a ; S 4 b ) and which determines a correction image ( 24.0 - 24. n ) from the recorded sample image ( 22 ) using the point spread function, wherein a difference between the correction image convolved with the point spread function and the recorded sample image ( 22 ) is minimized in the iteration process (S 4 ), and wherein in the iteration process (S 4 ) the passes through the iteration loop (S 4 a ; S 4 b ) are numbered with an ascending pass number (k) and each comprise a step size factor which is dependent on the pass number (k) of the respective pass and is determined without recourse to correction images.