  Deep learning approaches to the segmentation of magnetic resonance images
have shown significant promise in automating the quantitative analysis of brain
images. However, a continuing challenge has been its sensitivity to the
variability of acquisition protocols. Attempting to segment images that have
different contrast properties from those within the training data generally
leads to significantly reduced performance. Furthermore, heterogeneous data
sets cannot be easily evaluated because the quantitative variation due to
acquisition differences often dwarfs the variation due to the biological
differences that one seeks to measure. In this work, we describe an approach
using alternating segmentation and synthesis steps that adapts the contrast
properties of the training data to the input image. This allows input images
that do not resemble the training data to be more consistently segmented. A
notable advantage of this approach is that only a single example of the
acquisition protocol is required to adapt to its contrast properties. We
demonstrate the efficacy of our approaching using brain images from a set of
human subjects scanned with two different T1-weighted volumetric protocols.
