  Semantic segmentation of medical images is a crucial step for the
quantification of healthy anatomy and diseases alike. The majority of the
current state-of-the-art segmentation algorithms are based on deep neural
networks and rely on large datasets with full pixel-wise annotations. Producing
such annotations can often only be done by medical professionals and requires
large amounts of valuable time. Training a medical image segmentation network
with weak annotations remains a relatively unexplored topic. In this work we
investigate training strategies to learn the parameters of a pixel-wise
segmentation network from scribble annotations alone. We evaluate the
techniques on public cardiac (ACDC) and prostate (NCI-ISBI) segmentation
datasets. We find that the networks trained on scribbles suffer from a
remarkably small degradation in Dice of only 2.9% (cardiac) and 4.5% (prostate)
with respect to a network trained on full annotations.
