Abstract: A method and a device are disclosed for automated detection of at least one part of the central line of at least one portion of a tubular tissue structure. In at least one embodiment of the method, characteristic landmarks of the tubular tissue structure are detected in a 3D data record of the tubular tissue structure; the detected characteristic landmarks of the tubular tissue structure and landmarks of a model of landmarks, which model belongs to the tubular tissue structure and takes into account the position of the landmarks relative to one another, are related to one another such that at least one portion of the tubular tissue structure is identified in the 3D data record of the tubular tissue structure. The central line of the identified portion of the tubular tissue structure is determined at least in part on the basis of at least one detected characteristic landmark and/or at least one landmark of the model of landmarks.

Abstract: A method is disclosed for determining a branching point within a hollow organ in image data representing the spatial structure thereof. In at least one embodiment, the method includes setting a start point within the hollow organ; determining at least one local threshold which corresponds to the presence of a wall of the hollow organ; carrying out a region growing method using the threshold; analyzing the connectivity of a number of outer growth layers; and localizing a branching on the basis of the connectivity analysis. Moreover, at least one embodiment of the invention relates to a correspondingly designed branching determination unit.

Abstract: A method and a computer system are disclosed for automatic vectorization of the profile of a vessel tree and at least one of its properties on the basis of tomographic images of an examined patient. In at least one embodiment, using previously established location probabilities of landmarks in the vessel tree, there is an automatic determination of a plurality of distinctive landmarks in the current tomographic image data record of the patient, a registration of the current tomographic image data record to the statistical vessel model, an automatic determination of previously unidentified landmarks in the registered tomographic image data record using characteristic identification features of the previously unidentified landmarks from the statistical vessel model and the statistical location probability thereof, and a determination of at least one current vessel model using the identified landmarks and at least one vessel property at and/or between the identified landmarks.

Abstract: A method is disclosed for establishing at least one change in a tubular tissue structure in a living being from a first time to a second time, which differs from the first, wherein the midline of the tubular tissue structure is respectively determined in a provided first volume data record, generated at the first time, of the tubular tissue structure in the living being, and in a provided second volume data record, generated at the second time, which differs from the first, of the tubular tissue structure.

Abstract: A method is disclosed for highlighting local characteristics in anatomical volume renderings of vessel structures and a computer system is disclosed for carrying out this method.

Abstract: A method is disclosed for establishing at least one change in a tubular tissue structure in a living being from a first time to a second time, which differs from the first, wherein the midline of the tubular tissue structure is respectively determined in a provided first volume data record, generated at the first time, of the tubular tissue structure in the living being, and in a provided second volume data record, generated at the second time, which differs from the first, of the tubular tissue structure.

Abstract: A method and a computer system are disclosed for automatic vectorization of the profile of a vessel tree and at least one of its properties on the basis of tomographic images of an examined patient. In at least one embodiment, using previously established location probabilities of landmarks in the vessel tree, there is an automatic determination of a plurality of distinctive landmarks in the current tomographic image data record of the patient, a registration of the current tomographic image data record to the statistical vessel model, an automatic determination of previously unidentified landmarks in the registered tomographic image data record using characteristic identification features of the previously unidentified landmarks from the statistical vessel model and the statistical location probability thereof, and a determination of at least one current vessel model using the identified landmarks and at least one vessel property at and/or between the identified landmarks.

Abstract: A method and a device are disclosed for automated detection of at least one part of the central line of at least one portion of a tubular tissue structure. In at least one embodiment of the method, characteristic landmarks of the tubular tissue structure are detected in a 3D data record of the tubular tissue structure; the detected characteristic landmarks of the tubular tissue structure and landmarks of a model of landmarks, which model belongs to the tubular tissue structure and takes into account the position of the landmarks relative to one another, are related to one another such that at least one portion of the tubular tissue structure is identified in the 3D data record of the tubular tissue structure. The central line of the identified portion of the tubular tissue structure is determined at least in part on the basis of at least one detected characteristic landmark and/or at least one landmark of the model of landmarks.

Abstract: A method is disclosed for highlighting local characteristics in anatomical volume renderings of vessel structures and a computer system is disclosed for carrying out this method.

Abstract: A method is disclosed for determining a branching point within a hollow organ in image data representing the spatial structure thereof. In at least one embodiment, the method includes setting a start point within the hollow organ; determining at least one local threshold which corresponds to the presence of a wall of the hollow organ; carrying out a region growing method using the threshold; analyzing the connectivity of a number of outer growth layers; and localizing a branching on the basis of the connectivity analysis. Moreover, at least one embodiment of the invention relates to a correspondingly designed branching determination unit.