domain knowledge for accurately labeling regions may be restored partially by automated segmentation approaches. The maximum accuracy of the segmentation result is therefore highly dependent on the individual set of rules or amount of training data available. If the segmentation task is sufficiently complex, a perfect result may not be reachable. (3) Interactive approaches aim at a fast and exact segmentation by combining substantial assistance by the system with knowledge about a very good estimate of the true tumor extent provided by trained physicians during the segmentation process [@olabarriaga1997setting]. In contrast to fully automated solutions, prior knowledge is (also) provided during the segmentation process. Although, interactive approaches are also costly in terms of manual labor to some extent, they can supersede fully automated techniques in terms of accuracy. Due to their exact segmentation capabilities, interactive segmentation techniques are frequently chosen to outline pathologies during imaging assisted medical procedures, like hepatocellular carcinomata during trans-catheter arterial chemoembolization (see **Sec.**\[sec:tace\]).

Evaluation of Image Segmentation Systems
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Performance evaluation is one of the most important aspects during the continuous improvement of systems and methodologies. With non-interactive computer vision and machine learning systems for image segmentation, an objective comparison of systems can be achieved by 