segmentation can be defined as the partitioning of an image into a finite number of semantically non-overlapping regions. A semantic label can be assigned to each region. In medical imaging, each individual region of a patients’ abdominal tissue might be regarded as healthy or cancerous. Segmentation systems can be grouped into three principal categories, each differing in the degree of involvement of an operating person (user): manual, automatic, and interactive. (1) During manual tumor segmentation, a user provides all elements $i$ in the image grid which have neighboring elements $N(i)$ of different labels than $i$. The system then utilizes this closed curve contour line information to infer the labels for remaining image elements via simple region growing. This minimal assistance by the system causes the overall segmentation process of one lesion to take up to several minutes of user interaction time. However, reaching an appropriate or even perfect segmentation result (despite noteworthy difference [@becker2017increased]) is feasible [@kim2016interobserver; @hong2014interobserver]. In practice, few manual segmentations are performed by domain experts, in order to utilize the results as a reference standard in radiotherapy planning [@moltz2011analysis]. (2) A fully automated approach does not involve a user’s interference with the system. The introduced deficiency in 