LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration

Autores/as

  • Ubaldo Ramon Julvez Computer Science for Complex System Modeling (COSMOS) - I3A
  • Mónica Hernández Giménez Computer Science for Complex System Modeling (COSMOS) - I3A
  • Elvira Mayordomo Cámara Computer Science for Complex System Modeling (COSMOS) - I3A

Resumen

In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks. We have successfully implemented two models with stationary and EPDiff constrained non-stationary parameterizations of diffeomorphisms. Our approach has shown a competitive performance with respect to benchmark supervised and model-based methods.

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Publicado

2021-11-12

Número

Sección

Artículos (Tecnologías de la Información y las Comunicaciones)