  Despite their outstanding accuracy, semi-supervised segmentation methods
based on deep neural networks can still yield predictions that are considered
anatomically impossible by clinicians, for instance, containing holes or
disconnected regions. To solve this problem, we present a Context-aware Virtual
Adversarial Training (CaVAT) method for generating anatomically plausible
segmentation. Unlike approaches focusing solely on accuracy, our method also
considers complex topological constraints like connectivity which cannot be
easily modeled in a differentiable loss function. We use adversarial training
to generate examples violating the constraints, so the network can learn to
avoid making such incorrect predictions on new examples, and employ the
Reinforce algorithm to handle non-differentiable segmentation constraints. The
proposed method offers a generic and efficient way to add any constraint on top
of any segmentation network. Experiments on two clinically-relevant datasets
show our method to produce segmentations that are both accurate and
anatomically-plausible in terms of region connectivity.
