Abstract
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.
U. Ramon, M. Hernandez and E. Mayordomo—With the ADNI Consortium.
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Acknowledgement
This work was partially supported by the national research grant TIN2016-80347-R (DIAMOND project),PID2019-104358RB-I00 (DL-Ageing project), and Government of Aragon Group Reference \(T64\_20R\) (COSMOS research group). Ubaldo Ramon-Julvez’s work was partially supported by an Aragon Government grant. Project PID2019-104358RB-I00 granted by MCIN/AEI/10.13039/501100011033. We would like to thank Gary Christensen for providing the access to the NIREP database [34]. Data used in the preparation of this article were partially obtained from the Alzheimer’ s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Ramon, U., Hernandez, M., Mayordomo, E. (2022). LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham. https://doi.org/10.1007/978-3-031-11203-4_3
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