  A key feature of magnetic resonance (MR) imaging is its ability to manipulate
how the intrinsic tissue parameters of the anatomy ultimately contribute to the
contrast properties of the final, acquired image. This flexibility, however,
can lead to substantial challenges for segmentation algorithms, particularly
supervised methods. These methods require atlases or training data, which are
composed of MR image and labeled image pairs. In most cases, the training data
are obtained with a fixed acquisition protocol, leading to suboptimal
performance when an input data set that requires segmentation has differing
contrast properties. This drawback is increasingly significant with the recent
movement towards multi-center research studies involving multiple scanners and
acquisition protocols. In this work, we propose a new framework for supervised
segmentation approaches that is robust to contrast differences between the
training MR image and the input image. Our approach uses a generative
simulation model within the segmentation process to compensate for the contrast
differences. We allow the contrast of the MR image in the training data to vary
by simulating a new contrast from the corresponding label image. The model
parameters are optimized by a cost function measuring the consistency between
the input MR image and its simulation based on a current estimate of the
segmentation labels. We provide a proof of concept of this approach by
combining a supervised classifier with a simple simulation model, and apply the
resulting algorithm to synthetic images and actual MR images.
