SynthMorph: learning contrast-invariant registration without acquired images

Understand

We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI).

• While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair.
• Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training.
• We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features.