NimbleReg: A light-weight deep-learning framework for diffeomorphic image registration
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
This paper presents NimbleReg, a light-weight deep-learning (DL) framework
for diffeomorphic image registration leveraging surface representation of
multiple segmented anatomical regions. Deep learning has revolutionized image
registration but most methods typically rely on cumbersome gridded
representations, leading to hardware-intensive models. Reliable fine-grained
segmentations, that are now accessible at low cost, are often used to guide the
alignment. Light-weight methods representing segmentations in terms of boundary
surfaces have been proposed, but they lack mechanism to support the fusion of
multiple regional mappings into an overall diffeomorphic transformation.
Building on these advances, we propose a DL registration method capable of
aligning surfaces from multiple segmented regions to generate an overall
diffeomorphic transformation for the whole ambient space. The proposed model is
light-weight thanks to a PointNet backbone. Diffeomoprhic properties are
guaranteed by taking advantage of the stationary velocity field parametrization
of diffeomorphisms. We demonstrate that this approach achieves alignment
comparable to state-of-the-art DL-based registration techniques that consume
images.
