  Many recent delineation techniques owe much of their increased effectiveness
to path classification algorithms that make it possible to distinguish
promising paths from others. The downside of this development is that they
require annotated training data, which is tedious to produce.
  In this paper, we propose an Active Learning approach that considerably
speeds up the annotation process. Unlike standard ones, it takes advantage of
the specificities of the delineation problem. It operates on a graph and can
reduce the training set size by up to 80% without compromising the
reconstruction quality.
  We will show that our approach outperforms conventional ones on various
biomedical and natural image datasets, thus showing that it is broadly
applicable.
