 Critically, we show that input labels  need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data . We present extensive experiments demonstrating that this strategy enables registration of modalities not seen during training and surpasses the state of art in cross-contrast registration .
<clarity>  Critically, we show that input labels  need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data . We present extensive experiments demonstrating that this strategy enables registration of modalities not seen during training and surpasses the state of art in cross-contrast registration .
 Critically, we show that input labels need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance , albeit slightly less accurate, while alleviating the dependency on real data of any kind .