Brainlab AG

Disclosed is a computer-implemented method of determining an entry region for inserting elongated medical instruments such as trocars or laparoscopes into an anatomical body part such as the abdomen. The determination is made on the basis of a patient image and a known or assumed geometry of the medical instruments and a known position of the target region, and risk structures which shall be avoided by the medical instrument are determined either by user interaction from the patient image or automatically by matching the patient image with an atlas defining the position of such risk structures in an image-based model of the anatomical body part. On the basis of the position of the determined entry region, a robotic device may be controlled to attain a position which is suitable for automatic insertion of the medical instruments into the anatomical body part.

A method for determining the relative position between a first camera and a second camera used in a medical application, wherein the first camera captures a 2D image of a phantom, the second camera emits light onto the phantom and analyzes the reflected light, thus generating a 3D point cloud representing points on the surface of the phantom, and the phantom has a planar surface forming a background on which a plurality of 2D markers are formed, wherein one of the background and the 2D markers is reflective, thus reflecting light emitted by the second camera back to the second camera, and the other one is non-reflective, thus not reflecting light emitted by the second camera back to the second camera, the method involving that a) the first camera captures a 2D image of the phantom, b) the second camera generates a 3D point cloud representing the planar surface of the phantom, c) the 2D markers are identified in the 2D image, thus obtaining 2D marker data representing the locations of the 2D markers in the 2D image, d) the 2D markers are identified in the 3D point cloud using the property that points on a non-reflective part of the planar surface are identified as having a larger distance to the second camera than points on a reflective part of the planar surface, thus obtaining 3D marker data representing the locations of the 2D markers in a reference system of the second camera, and e) finding the relative position between the first camera and the second camera by applying a Perspective-n-Points algorithm on the 2D marker data and the 3D marker data.

The present invention relates to a head immobilization system for immobilizing a patient's head in a supine position of the patient, the system comprising: a support rail structure adapted to be coupled to a patient rest and extending at least on both lateral sides of the patient's head, a mask frame adapted to be coupled to at least one deformable upper mask sheet, wherein the mask frame is releasably connected to the support rail structure via a first interface section and a second interface section, with at least two pins protruding from the first interface section in a first direction, and at least two pin-receptions provided at the second interface section, wherein each one of the pin-receptions receives one of the pins, and a catch-mechanism for each pin-reception and each corresponding pin, which allows the pin to be pushed further into the pin-reception in the first direction, but which interlocks in case of an attempted withdrawal of the pin from the pin-reception in a second, opposite direction.

The present invention relates to a computer-implemented medical method, a computer program and a system ( 1 ) for controlling a display ( 6 ) in a medical environment based a user's ( 8 ) viewing direction and gestures performed by the user ( 8 ), particularly involving the use of AR-goggles ( 9 ).

Disclosed is a computer-implemented method for determining a target position of an X-ray device which encompasses acquiring image data describing an anatomical structure of a patient, for example, by means of a 3D scan, and registering the image data relative to a coordinate system of the patient, for example by means of a navigation system (embodied by registered image data). Furthermore, a trajectory of an implant positioned within the anatomical structure relative to the patient coordinate system is acquired (embodied by trajectory data). A target position of an X-ray device for acquiring an X-ray image of at least part of the implant is determined based on the registered image and the acquired trajectory of the implant (embodied by X-ray device position data).

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

Disclosed is a computer-implemented medical data processing method for determining an orientation of an electrode, the electrode being configured for electrically stimulating an anatomical structure of a patient and comprising a rotational orientation marker, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring (S 1.1 ), at the at least one processor, rotational image data describing two-dimensional medical images of the anatomical structure and the electrode, the two-dimensional medical images having been taken with a two-dimensional medical imaging apparatus during rotation of the medical imaging apparatus relative to the anatomical structure, the rotational image data further describing, for each of the two-dimensional medical images, an imaging perspective relative to the anatomical structure associated with the respective two-dimensional medical image; b) determining (S 1.2 ), by the at least one processor and based on the rotational image data, rotational orientation data describing the rotational orientation of the electrode in the reference system of the two-dimensional medical images; c) acquiring (S 1.3 ), at the at least one processor, tomographic image data describing a set of tomographic medical images of the anatomical structure; d) determining (S 1.4 ), by the at least one processor and based on the rotational image data and the tomographic image data and the rotational orientation data, electrode orientation data describing a rotational orientation of the electrode in a reference system of the tomographic medical image data.

By using the Al module, the method of the present invention calculates, i.e. predicts, the dependency C i (p i ) of a radiotherapy (RT) quality criterion C, from an adjustment of such a radiotherapy planning parameter p i . In this way, the decision making process in RT treatment plan optimization is streamlined by prediction of promising settings of one or more radiotherapy planning parameters p, before the actual time intensive iterative optimization process is carried out. This is achieved by applying an Al module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data, dose prescription and treatment indication data. Thus, a computer-implemented medical method of predicting a dependency C i (p i ) of a radiotherapy (RT) quality criterion C i from an adjustment of a radiotherapy planning parameter p, is presented. The method comprises the following steps of providing geometric patient data geometrically describing an area of a patient, which is to be irradiated according to a radiotherapy treatment plan (step S 1 ), providing dose prescription data and treatment indication data for said patient (step S 2 ), and predicting with a trained Artificial Intelligence (Al) module the dependency C i (p i ) of the radiotherapy quality criterion C i from the radiotherapy planning parameter p, when adjusting said radiotherapy planning parameter p i , thereby using the geometric patient data, the dose prescription data and the treatment indication data as input for the Al module (step S 3 ).

Disclosed is a computer-implemented methods of determining distributions of corrections for correcting the segmentation of medical image data, determining corrections for correcting the segmentation of medical image data, training a learning algorithm for determining a segmentation of a digital medical image, and determining a relation between an image representation of the anatomical body part in an individual medical image and a label to be associated with the image representation of the anatomical body part in the individual medical image using the trained machine learning algorithm. The methods encompass reading a plurality of corrections to image segmentations, wherein the corrections themselves may have been manually generated, transforming these corrections into a reference system which is not patient-specific such as an atlas reference system, conducting a statistical analysis of the correction, and applying the re-transformed result of the statistical analysis to patient images. The result of the statistical analysis may also be used to appropriately train a machine learning algorithm for automatic segmentation of patient images. The application of such a trained machine learning algorithm is also part of this disclosure.