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In this contribution, a semi-automatic segmentation algorithm for (medical)image analysis is presented. More precise, the approach belongs to the categoryof interactive contouring algorithms, which provide real-time feedback of thesegmentation result. However, even with interactive real-time contouringapproaches there are always cases where the user cannot find a satisfyingsegmentation, e.g. due to homogeneous appearances between the object and thebackground, or noise inside the object. For these difficult cases the algorithmstill needs additional user support. However, this additional user supportshould be intuitive and rapid integrated into the segmentation process, withoutbreaking the interactive real-time segmentation feedback. I propose a solutionwhere the user can support the algorithm by an easy and fast placement of oneor more seed points to guide the algorithm to a satisfying segmentation resultalso in difficult cases. These additional seed(s) restrict(s) the calculationof the segmentation for the algorithm, but at the same time, still enable tocontinue with the interactive real-time feedback segmentation. For a practicaland genuine application in translational science, the approach has been testedon medical data from the clinical routine in 2D and 3D.

Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science