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.
CITATION STYLE
Chen, J., Frey, E. C., & Du, Y. (2022). Unsupervised Learning of Diffeomorphic Image Registration via TransMorph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13386 LNCS, pp. 96–102). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-11203-4_11
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