Carl Zeiss Meditec Ag

Carl Zeiss Industrielle Messtechnik Gmbh

Stefan Saur

Christoph Hauger

Marco Wilzbach

Gerald Panitz

Christian Voigt

Fang You

David Dobbelstein

Holger Matz

Christopher Käsbach

Christoph Schaeff

Markus Philipp

Dominik Scherer

Christian Albrecht

Hendrik Burwinkel

Susanne Kohlhammer

Dominik Litsch

Patrick Hoyer

Anna Alperovich

Roland Guckler

Helge Jess

Christian Wojek

Alexander Freytag

Frank Rudolph

Martin Fanenbruck

Jonathan Essig

Andreas Raab

Nassir Navab

Steffen Urban

Carolin Schiele

Stefan Schute

Christopher Kaesbach

Marco Witzbach

Franziska Mathis-Ullrich

Adrian Samp

Zuzanna Derda

Alexander Urich

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. In the process, the ophthalmological data provided and the position of the intraocular lens are used as input values for the machine learning system with the second learning model.

Physically motivated machine learning system for an optimized intraocular lens calculation

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.

Machine-learning-based determining of refractive power for measures for correcting eyesight from OCT images

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 () 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 (-), which are associated with an increased probability of the presence of surgical instruments.

Tool-type agnostic assistance functionality for surgical operations

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.

Fusion of spatial and temporal context for location determination for visualization systems

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 consideration of the determined operational context. Further, the invention relates to a microscope.

Method for controlling a microscope, and microscope

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.

Method and system for determining a pose of at least one object in an operating theatre

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.

Method and system for optically detecting the pose of at least one instrument in an operating theater

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.

Microscopy system and method for operating the microscopy system

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.

Multi-task learning of white light photographs for a surgical microscope

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.

Machine-learning based IOL position determination

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.

Visualization system comprising an observation apparatus and an endoscope

A method and a device for generating a control signal for a controllable device are provided. The controllable device has an optical position detection system. At least two images of at least one spatial region are generated with at least one optical detection device of the optical position detection system. Markers are identified in the images and the control signal is generated when a relative position between at least two markers changes. In addition, a marker array and a controllable system are provided.

Method and device for generating a control signal, marker array and controllable system

A parameterization of a control algorithm of a surgical microscope is carried out on the basis of one or more context parameters of an operation. On the basis of the parameterization, the control algorithm then is applied to one or more state indicators associated with a first setting of the surgical microscope in order thus to obtain a second setting of the surgical microscope. By way of example, the parameterization can comprise one or more prioritization operations.

Context-dependent control of a surgical microscope

An eye surgery surgical system includes a device for displaying a relative position of a section of a surgery object in a 3D reconstruction of a region of an eye, a device for continuously providing at least two data records relating to at least partly overlapping portions of the region of the eye and of the section of the surgery object to a computer, a computer program for continuously ascertaining the relative position of the section of the surgery object and continuously ascertaining the 3D reconstruction of the eye from the data records provided. The program includes a first routine for continuously ascertaining the relative position of the section of the surgery object and the 3D reconstruction of the region of the eye from the data records provided via a registration method and a second routine for adapting the registration method based on a criterion.

Eye surgery surgical system having an OCT device and computer program and computer-implemented method for continuously ascertaining a relative position of a surgery object

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.

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Aalen, Germany

Stefan Saur