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 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 for assisting a neurosurgical operation, wherein at least one image representation of an operating region on a patient is captured by means of a medical visualization system wherein categorical position information describing an anatomical position of a tumor present in the operating region is acquired and/or obtained, wherein the at least one captured image representation and the acquired and/or obtained categorical position information are supplied to a trained machine learning model as input data, wherein a tumor type is predicted by means of the trained machine learning model using the at least one captured image representation and the acquired and/or obtained categorical position information as a starting point, wherein the trained machine learning model provides tumor type information describing the tumor type as output data, and wherein the tumor type information is output. The invention further relates to an arrangement for assisting a neurosurgical operation.

Abstract: A generic reference model for a planned operation defines a multiplicity of landmarks that describe the planned operation. State data that describe a specific operation are received, for example by a surgical microscope, while performing a specific operation on the basis of the planned operation. Then the generic reference model is individualized in relation to the specific operation on the basis of the state data by virtue of adapting one or more landmarks of the multiplicity of landmarks to the specific operation.

Abstract: A method for training and using a machine learning system for a differentiation between healthy and diseased tissue during a microsurgical intervention is described. In this case, the method comprises: receiving training data and associated annotation data for training a machine learning system, training the machine learning system, which after training is configured for a prediction of a probability value and a prediction of a trustworthiness value, from which a control signal for a surgery assistance system is derivable, which is usable during a later application during a microsurgical operation, and storing parameter values of the trained machine learning model.

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 techniques for controlling an assistance functionality for a surgical operation on a patient. A machine-trained algorithm is used to obtain a map (76) of an operation region. This map can be used to control an assistance functionality in the context of the surgical operation. The map is indicative of one or more activity regions (101-103), which are associated with an increased probability of the presence of surgical instruments.

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: 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 for controlling a microscope, wherein a voice information item is captured by means of at least one microphone, wherein, based on the captured voice information item, a command assigned to the voice information item is recognized from a plurality of commands, wherein an operational context, in which the microscope is when the voice information item is captured, is determined based on at least one context information item, wherein at least one microscope action is determined in consideration of the recognized command, and wherein at least one control command is generated to control the microscope to carry out the at least one determined microscope action, and wherein the at least one microscope action is carried out by controlling the microscope by means of the generated at least one control command, wherein the associated command is recognized in consideration of the determined operational context, and/or wherein the at least one microscope action is determined in considerati

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 method and system include an image of a body part of a patient is captured by a camera of a medical visualization system, a trained machine learning method and/or a method of computer vision is used to estimate a three-dimensional surface profile of the body part in a coordinate system of the camera using the captured image as a starting point, a scaling factor of the three-dimensional surface profile is determined and/or estimated, and preoperatively acquired three-dimensional patient data that are available in a patient coordinate system are fitted to the estimated three-dimensional surface profile. A transformation rule between the coordinate system of the camera and the patient coordinate system is determined using a resultant fit result as a starting point, the fitting and/or the determining of the transformation rule is performed taking into account the determined and/or estimated scaling factor, and the determined transformation rule is provided.

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 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 visualization system includes an observation apparatus having a first image recording device to observe an operation region with a first observation plane, and an endoscope having a probe and a second image recording device to observe the operation region with a second observation plane. A display device represents a first image recorded by the first image recording device in a first orientation and a second image recorded by the second image recording device in a second orientation. The visualization system further includes a tracking system to determine an orientation of the endoscope relative to the observation apparatus and a controller configured to transform the second image based on the orientation of the endoscope relative to the observation apparatus.

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: Techniques which relate to an automatic configuration of one or more components of a surgical microscopy system for imaging a moving object are disclosed. In the described techniques, the initially determined target configuration of the surgical microscopy system is selectively adapted if a movement of the moving object is detected; this selective adaptation takes place when the movement is within a predefined spatial limit and/or a predefined temporal limit.

Abstract: A description is given of techniques in connection with carrying out an assistance functionality for a surgical microscopy system. The assistance functionality—for example auto-centring or measurement of a surgical instrument—is carried out in connection with multiple objects that are depicted in an image captured by way of the surgical microscopy system. Prioritization information for the objects is furthermore also taken into consideration, for example in order to perform classification into relevant and irrelevant objects.

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: 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.