The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature on deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the state of the art within the LDDMM paradigm. We have successfully implemented two models with the stationary and the EPDiff-constrained non-stationary parameterizations of diffeomorphisms. Our unsupervised learning approach has shown competitive performance with respect to benchmark supervised learning and model-based methods.
CITATION STYLE
Ramon, U., Hernandez, M., & Mayordomo, E. (2022). LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13386 LNCS, pp. 18–28). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-11203-4_3
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