  The manual outlining of hepatic metastasis in (US) ultrasound acquisitions
from patients suffering from pancreatic cancer is common practice. However,
such pure manual measurements are often very time consuming, and the results
repeatedly differ between the raters. In this contribution, we study the
in-depth assessment of an interactive graph-based approach for the segmentation
for pancreatic metastasis in US images of the liver with two specialists in
Internal Medicine. Thereby, evaluating the approach with over one hundred
different acquisitions of metastases. The two physicians or the algorithm had
never assessed the acquisitions before the evaluation. In summary, the
physicians first performed a pure manual outlining followed by an algorithmic
segmentation over one month later. As a result, the experts satisfied in up to
ninety percent of algorithmic segmentation results. Furthermore, the
algorithmic segmentation was much faster than manual outlining and achieved a
median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately,
the algorithm enables a fast and accurate segmentation of liver metastasis in
clinical US images, which can support the manual outlining in daily practice.
