Abstract:
Disclosed is a computer-implemented method of training a likelihood-based computational model for determining the position of an image representation of an annotated anatomical structure in a two-dimensional x-ray image, wherein the method encompasses inputting medical DRRs together with annotation to a machine learning algorithm to train the algorithm, i.e. to generate adapted leamable parameters of the machine learning model. The annotations may be derived from metadata associated with the DRRs or may be included in atlas data which is matched with the DRRs to establish a relation between the annotations included in the atlas data and the DRRs. The thus generated machine learning algorithm may then be used to analyse clinical or synthesized DRRs so as to appropriately add annotations to those DRRs and/or identify the position of an anatomical structure in those DRRs.
Abstract:
An adaptor for receiving a navigated structure, wherein the navigated structure is at least a part of a medical object which carries an object reference, and for being connected to a registration tool in order to register the navigated structure in a medical navigation system, the adaptor comprising at least two adaptor parts which, in an assembled state, form a structure receiving recess in the shape of the navigated structure and an adaptor coupling part for connecting the adaptor to the registration tool in a predetermined relative position.