 While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on . In contrast, our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training.
<clarity>  While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on . In contrast, our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training.
 While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training.