  Instance segmentation is an important computer vision problem which remains
challenging despite impressive recent advances due to deep learning-based
methods. Given sufficient training data, fully supervised methods can yield
excellent performance, but annotation of ground-truth data remains a major
bottleneck, especially for biomedical applications where it has to be performed
by domain experts. The amount of labels required can be drastically reduced by
using rules derived from prior knowledge to guide the segmentation. However,
these rules are in general not differentiable and thus cannot be used with
existing methods. Here, we relax this requirement by using stateless actor
critic reinforcement learning, which enables non-differentiable rewards. We
formulate the instance segmentation problem as graph partitioning and the actor
critic predicts the edge weights driven by the rewards, which are based on the
conformity of segmented instances to high-level priors on object shape,
position or size. The experiments on toy and real datasets demonstrate that we
can achieve excellent performance without any direct supervision based only on
a rich set of priors.
