Unsupervised Learning of Diffeomorphic Image Registration via TransMorph

In this work, we propose a learning-based framework for unsupervised and end-to-end learning of diffeomorphic image registration. Specifically, the proposed network learns to produce and integrate time-dependent velocity fields in an LDDMM setting. The proposed method guarantees a diffeomorphic transformation and allows the transformation to be easily and accurately inverted. We also showed that, without explicitly imposing a diffeomorphism, the proposed network can provide a significant performance gain while preserving the spatial smoothness in the deformation. The proposed method outperforms the state-of-the-art registration methods on two widely used publicly available datasets, indicating its effectiveness for image registration. The source code of this work is available at: https://bit.ly/3EtYUFN.