  Brain extraction is a fundamental step for most brain imaging studies. In
this paper, we investigate the problem of skull stripping and propose
complementary segmentation networks (CompNets) to accurately extract the brain
from T1-weighted MRI scans, for both normal and pathological brain images. The
proposed networks are designed in the framework of encoder-decoder networks and
have two pathways to learn features from both the brain tissue and its
complementary part located outside of the brain. The complementary pathway
extracts the features in the non-brain region and leads to a robust solution to
brain extraction from MRIs with pathologies, which do not exist in our training
dataset. We demonstrate the effectiveness of our networks by evaluating them on
the OASIS dataset, resulting in the state of the art performance under the
two-fold cross-validation setting. Moreover, the robustness of our networks is
verified by testing on images with introduced pathologies and by showing its
invariance to unseen brain pathologies. In addition, our complementary network
design is general and can be extended to address other image segmentation
problems with better generalization.
