IEEE Transactions on Medical Imaging, 41 (3), 543-558, 2022
Summary
Recent advances in deep learning have dramatically enhanced the
accuracy and efficiency of image registration. Yet, the dependency of these
algorithms on the specific training data remains an unsolved problem,
characterized by inaccurate registration of out-of-distribution images. To
address this data dependency, we propose SynthMorph, a strategy for learning
contrast-invariant registration without acquired images. By exposing
networks to a landscape of unrealistic synthetic data at training, SynthMorph
enables unprecedented robustness across a range of real image types and greatly
alleviates the need for retraining models.