  We propose a method for interactive boundary extraction which combines a
deep, patch-based representation with an active contour framework. We train a
class-specific convolutional neural network which predicts a vector pointing
from the respective point on the evolving contour towards the closest point on
the boundary of the object of interest. These predictions form a vector field
which is then used for evolving the contour by the Sobolev active contour
framework proposed by Sundaramoorthi et al. The resulting interactive
segmentation method is very efficient in terms of required computational
resources and can even be trained on comparatively small graphics cards. We
evaluate the potential of the proposed method on both medical and non-medical
challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data
set.
