  Deep learning-based approaches to delineating 3D structure depend on accurate
annotations to train the networks. Yet, in practice, people, no matter how
conscientious, have trouble precisely delineating in 3D and on a large scale,
in part because the data is often hard to interpret visually and in part
because the 3D interfaces are awkward to use. In this paper, we introduce a
method that explicitly accounts for annotation inaccuracies. To this end, we
treat the annotations as active contour models that can deform themselves while
preserving their topology. This enables us to jointly train the network and
correct potential errors in the original annotations. The result is an approach
that boosts performance of deep networks trained with potentially inaccurate
annotations. Code has been released at
https://github.com/doruk-oner/AdjustingAnnotationswithSnakes.
