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Preprint
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
Posted to a preprint site 06/14/2021
ArXiv.org
Unsupervised learning-based medical image registration approaches have
witnessed rapid development in recent years. We propose to revisit a commonly
ignored while simple and well-established principle: recursive refinement of
deformation vector fields across scales. We introduce a recursive refinement
network (RRN) for unsupervised medical image registration, to extract
multi-scale features, construct normalized local cost correlation volume and
recursively refine volumetric deformation vector fields. RRN achieves state of
the art performance for 3D registration of expiratory-inspiratory pairs of CT
lung scans. On DirLab COPDGene dataset, RRN returns an average Target
Registration Error (TRE) of 0.83 mm, which corresponds to a 13% error reduction
from the best result presented in the leaderboard. In addition to comparison
with conventional methods, RRN leads to 89% error reduction compared to
deep-learning-based peer approaches.