Abstract: The invention relates to a method for operating a stereoscopic medical microscope, wherein deteriorated and/or invalid calibration data are recognized, wherein for this purpose mutually corresponding image representations of at least one feature arranged in capture regions of cameras of a stereo camera system of the medical microscope are captured by means of the cameras, the captured image representations are evaluated by means of feature-based image processing, wherein the at least one feature is recognized in this case in the captured image representations and a misalignment and/or a decalibration of the cameras of the stereo camera system are/is recognized on the basis of the at least one feature recognized; and wherein at least one measure is carried out depending on an evaluation result. Furthermore, the invention relates to a medical microscope.

Abstract: The invention relates to a medical optical system. The medical optical system comprises: —a microendoscope (3) for capturing histological images, each of which displays a microscopic tissue section (16) of a macroscopic tissue region (15) with a tumor (23); and—a classification device (31) for classifying the macroscopic tissue sections (16) displayed in the histological images as at least one respective tissue section that represents the tumor (23) or a tissue section that represents healthy tissue and for outputting a classification result for each classified microscopic tissue section (16). The medical optical system additionally comprises a combination device (37) which generates a macroscopic classification image (43) by combining the classification results, said classification image representing the location of the tumor (23) in the macroscopic tissue region (15).

Abstract: A head-mounted visualization unit is proposed having an at least partially light-transmissive optical system, wherein the optical system has a first optical channel, which is assigned to a first eye of a user of the head-mounted visualization unit, and a second optical channel, which is assigned to a second eye of the user, wherein the first optical channel is substantially transmissive to optical radiation of a first polarization and substantially opaque to optical radiation of a second polarization, with the first polarization substantially being orthogonal to the second polarization, wherein the second optical channel is substantially transmissive to optical radiation of the second polarization and substantially opaque to optical radiation of the first polarization, wherein at least in the first optical channel a polarizer and a light attenuator are arranged, and wherein the light attenuator is arranged downstream of the polarizer in a direction toward the first eye of the user.

Abstract: The invention relates to a head-mounted visualization system having a wearing system, at least one light-transmissive optical system, an image generation device designed to generate image information based on the image data supplied to the image generation device, wherein the optical system is designed to supply image information generated by the image generation device to a person wearing the visualization system, and a polarization unit designed to polarize light penetrating the optical system differently in two spatial regions. The invention further relates to a surgical visualization system having such a head-mounted visualization system and to a visualization method for a surgical environment.

Abstract: A method of operating a surgical microscope includes detecting a position of a user, and setting a rotation angle of a camera about its main axis such that it is between a first angle and a second angle. The first angle is the rotation angle required to display a first straight object as a vertical line, and the second angle is the rotation angle required to display a second straight object as a horizontal line. The first object extends along a first line arranged in a vertical plane containing a line connecting the position of the user with the field of view. The first line is horizontal and traverses the field of view. The second object extends along a second line traversing the field of view. The second line is horizontal and perpendicular to the first line.

Abstract: The invention relates to a computer-implemented method and a corresponding system for a machine-learning-supported determining of refractive power for a measure for correcting the eyesight of a patient. The method involves providing a scan result of an eye, wherein the scan result represents an image of an anatomical structure of the eye. The method also involves supplying the scan result as input data to a first machine-learning system in the form of a convolutional neural network, and using output values of the first machine-learning system as input data for a second machine-learning system in the form of a multi-layer perceptron, and a target refraction value for the second machine-learning system is used as an additional input value for the second machine-learning system. Finally, the method involves determining parameters for the measure for correcting the eyesight of a patient via an immediate and direct cooperation of the first machine-learning system and the second machine-learning system.

Abstract: The invention relates to a computer-implemented method for a machine learning-supported processing pipeline for determining parameter values for an intraocular lens to be inserted. The method comprises providing a scan result of an eye. The scan result is an image of an anatomical structure of the eye. The method further comprises determining biometric data of the eye from the scan results of an eye and using a first, trained machine learning system for determining a final position of an intraocular lens to be inserted, ophthalmological data being used as input data for the first machine learning system. The method further comprises determining a first optical power of the intraocular lens to be inserted, which is based on a physical model in which the determined final position of the intraocular lens and the determined biometric data are used as input variables for the physical model.

Abstract: A method for creating a high-resolution image of an object from low-resolution images of the object is provided. Both the low-resolution images and the high-resolution image are composed of a pixel grid. An image recording device successively records low-resolution images, in which pitches of the grid points of the pixel grid are increased in one image dimension in comparison with the pitches of the grid points of the pixel grid in the high-resolution image to be created. A data processing system registers the low-resolution images with respect to one another to obtain registered images which are superimposed to obtain the high-resolution image. The grid points of the low-resolution images and the grid points of the high-resolution image have same dimensions and the data processing system uses image information obtained from different positions of the object relative to the grid points in the individual low-resolution images to create the high-resolution images.

Abstract: A computer-implemented method for determining the refractive power of an intraocular lens to be inserted is presented. The method includes generating first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens and training the machine learning system by means of the first training data generated, for the purposes of forming a first learning model for determining the refractive power. Furthermore, the method includes training the machine learning system, which was trained using the first training data, using clinical ophthalmological training data for forming a second learning model for determining the refractive power and providing ophthalmological data of a patient and an expected position of the intraocular lens to be inserted. Moreover, the method includes predicting the refractive power of the intraocular lens to be inserted by means of the trained machine learning system and the second learning model.

Abstract: The invention relates to a computer-assisted method for position determination for an intraocular lens supported by machine learning. The method comprises providing a scan result for an eye. The scan result here represents an image of an anatomical structure of the eye. The method further comprises use of a trained machine learning system for the direct determination of a final location of an intraocular lens to be fitted, wherein digital data of the scan of the eye is used as the input data for the machine learning system.

Abstract: A computer-implemented method for generating a control signal by locating at least one instrument by way of a combination of machine learning systems on the basis of digital images is described. In this case, the method includes determining parameter values of a movement context by using the at least two digital images and determining an influence parameter value which controls an influence of one of the digital images and the parameter values of the movement context on the input data which are used within a first trained machine learning system, which has a first learning model, for generating the control signal.

Abstract: The invention relates to a method and a system for determining a pose of at least one object in an operating theatre, in a reference coordinate system of a pose detection device of a surgical microscope, involving the determination of the pose of the object by way of a movably arranged microscope-external pose detection device in a first coordinate system, the first coordinate system being a coordinate system that is arranged to be stationary relative to the operating theatre, the determination of the pose of the reference coordinate system by the non-stationary microscope-external pose detection device in the first coordinate system, and the transformation of the pose of the object from the first coordinate system into the reference coordinate system of the pose detection device of the surgical microscope.

Abstract: A system and a method for optically detecting a pose of at least one instrument in an operating theater are provided. The method includes determining pose information of the at least one instrument with an optical pose detection device of a surgical microscope or with a microscope-external optical pose detection device, determining whether the pose information is biunique or whether a biunique determination of the pose of the instrument is possible, and evaluating additional information for determining additional pose information, at least for a case where no biunique determination of the pose is possible.

Abstract: A computer-implemented method for predicting digital images in the form of a digital fluorescence representation together with a further derived representation by means of a combined machine learning system is described. The method comprises providing a first digital image of a tissue sample that was recorded under white light by means of a microsurgical optical system with a digital image recording unit, and predicting a second digital image of the tissue sample in a fluorescence representation and a further representation, which has optical indications about diseased tissue elements. This is done by means of a previously trained combined machine learning system comprising a trained combined machine learning model for predicting the second digital image of the tissue sample in the fluorescence representation and the further representation.

Abstract: The invention relates to a method for determining information for distinguishing between tissue fluid cells and tissue cells in a high-resolution image (34) of a tissue area. In the method, images (33A-E) stored temporarily with a low resolution and a high image rate before the high-resolution image (34) is recorded are accessed and the information for distinguishing between tissue fluid cells and tissue cells is obtained from the temporarily stored images (33A-E) with the low resolution and the high image rate.

Abstract: A microscopy system includes a microscope, a stand configured to mount the microscope and including a drive device configured to move the microscope, a detection device configured to detect a spatial position of a target fastened to a body part or to an instrument, wherein the position detection device includes the target with at least one marker element and an image capture device configured to optically capture the target. The microscopy system further includes at least one control device configured to operate the microscopy system according to the detected position of the target, wherein the position detection device is configured to determine the position of the target by evaluating a two-dimensional image of the image capture device. In addition, a method for operating the microscopy system is provided.

Abstract: A computer-implemented method for recognizing deviations from plan parameters during an ophthalmological operation is described, the method including: providing video sequences of cataract operations, the video sequences having been recorded by means of an image recording apparatus, training a machine learning system using the video sequences provided and also, in each case, a planned refractive power of an intraocular lens to be inserted during a cataract operation and a target refraction value following the cataract operation as training input data and associated prediction results in the form of an actual refraction value following the cataract operation to form a machine learning model for predicting the actual refraction value following the cataract operation, and persistently storing parameter values of the trained machine learning system.

Abstract: A method of operating a surgical microscope includes detecting an amount of movement of a body portion of a user, and performing movements of a camera and changes in a magnification based on the detected movements of the body portion. The amounts of these movements and changes decrease with increasing magnification provided by the surgical microscope, they decrease with decreasing distance of the body portion of the user from the display, and they decrease with decreasing distance of the cameras from the field of view of the cameras.

Abstract: A method and a system for generating an assistance function for an ophthalmological surgery are presented. The method includes capturing digital image data of a surgical microscope, which were generated during an ophthalmological surgery by an image sensor and which are annotated. The method furthermore includes capturing sensor data of a phaco system, which were generated during the ophthalmological surgery by a sensor of the phaco system and which are annotated, wherein the annotated sensor data and the annotated digital image data have synchronized timestamps and wherein the annotations refer in indicative fashion to a state of an ophthalmological surgery.

Abstract: A microscopy system includes a microscope, a stand configured to mount the microscope and including a drive device configured to move the microscope, a detection device configured to detect a spatial position of a target fastened to a body part or to an instrument, wherein the position detection device includes the target with at least one marker element and an image capture device configured to optically capture the target. The microscopy system further includes at least one control device configured to operate the microscopy system according to the detected position of the target, wherein the position detection device is configured to determine the position of the target by evaluating a two-dimensional image of the image capture device. In addition, a method for operating the microscopy system is provided.