  Accurate automatic segmentation of brain anatomy from
$T_1$-weighted~($T_1$-w) magnetic resonance images~(MRI) has been a
computationally intensive bottleneck in neuroimaging pipelines, with
state-of-the-art results obtained by unsupervised intensity modeling-based
methods and multi-atlas registration and label fusion. With the advent of
powerful supervised convolutional neural networks~(CNN)-based learning
algorithms, it is now possible to produce a high quality brain segmentation
within seconds. However, the very supervised nature of these methods makes it
difficult to generalize them on data different from what they have been trained
on. Modern neuroimaging studies are necessarily multi-center initiatives with a
wide variety of acquisition protocols. Despite stringent protocol harmonization
practices, it is not possible to standardize the whole gamut of MRI imaging
parameters across scanners, field strengths, receive coils etc., that affect
image contrast. In this paper we propose a CNN-based segmentation algorithm
that, in addition to being highly accurate and fast, is also resilient to
variation in the input $T_1$-w acquisition. Our approach relies on building
approximate forward models of $T_1$-w pulse sequences that produce a typical
test image. We use the forward models to augment the training data with test
data specific training examples. These augmented data can be used to update
and/or build a more robust segmentation model that is more attuned to the test
data imaging properties. Our method generates highly accurate, state-of-the-art
segmentation results~(overall Dice overlap=0.94), within seconds and is
consistent across a wide-range of protocols.
