Understanding Domain-Shift Immunity in Deep Deformable Registration
TL;DR
It is shown that domain-shift immunity is an inherent, largely architecture-agnostic property of deep de-formable registration when trained with a robust pipeline and offered a principled explanation for the cross-domain generalizability of deep registration networks.
Abstract
Deep learning has achieved remarkable success in deformable image registration, yet the visual information that drives deformation estimation remains poorly understood. Rather than pursuing incremental performance improvements, this work investigates the fundamental source of robustness in deep registration models. Using diverse, domain-agnostic synthetic datasets, we decouple deformation learning from application-specific appearance and show that domain-shift immunity is an inherent, largely architecture-agnostic property of deep de-formable registration when trained with a robust pipeline. To identify the mechanism underlying this immunity, we compare models trained directly on raw image intensities with models operating exclusively on local feature representations extracted by a fixed, pre-defined feature extractor. The comparable performance of these models provides strong empirical evidence that deformation estimation is governed primarily by local structural features, rather than global, domain-specific appearance cues. These findings offer a principled explanation for the cross-domain generalizability of deep registration networks and point toward feature-centric designs for domain-independent registration.