Klaus W. G. Eichhorn

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Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic(More)
Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different(More)
Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students(More)
— In this video we show our current prototype for robot assisted endoscopy. The system requires only few and simple instructions from the surgeon, in order to guide the endoscope in an intelligent, autonomous, and safe way: The surgeon tells what to do and the robot decides how to carry out the task by choosing the best manipulation primitive in every(More)
We present a model-driven approach to the segmentation of nasal cavity and paranasal sinus boundaries. Based on computed tomog-raphy data of a patients head, our approach aims to extract the border that separates the structures of interest from the rest of the head. This three-dimensional region information is useful in many clinical applications , e.g.(More)
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