  In interactive medical image segmentation, anatomical structures are
extracted from reconstructed volumetric images. The first iterations of user
interaction traditionally consist of drawing pictorial hints as an initial
estimate of the object to extract. Only after this time consuming first phase,
the efficient selective refinement of current segmentation results begins.
Erroneously labeled seeds, especially near the border of the object, are
challenging to detect and replace for a human and may substantially impact the
overall segmentation quality. We propose an automatic seeding pipeline as well
as a configuration based on saliency recognition, in order to skip the
time-consuming initial interaction phase during segmentation. A median Dice
score of 68.22% is reached before the first user interaction on the test data
set with an error rate in seeding of only 0.088%.
