  We present Recurrence with Correlation Network (RWCNet), a medical image
registration network with multi-scale features and a cost volume layer. We
demonstrate that these architectural features improve medical image
registration accuracy in two image registration datasets prepared for the
MICCAI 2022 Learn2Reg Workshop Challenge. On the large-displacement National
Lung Screening Test (NLST) dataset, RWCNet is able to achieve a total
registration error (TRE) of 2.11mm between corresponding keypoints without
instance fine-tuning. On the OASIS brain MRI dataset, RWCNet is able to achieve
an average dice overlap of 81.7% for 35 different anatomical labels. It
outperforms another multi-scale network, the Laplacian Image Registration
Network (LapIRN), on both datasets. Ablation experiments are performed to
highlight the contribution of the various architectural features. While
multi-scale features improved validation accuracy for both datasets, the cost
volume layer and number of recurrent steps only improved performance on the
large-displacement NLST dataset. This result suggests that cost volume layer
and iterative refinement using RNN provide good support for optimization and
generalization in large-displacement medical image registration. The code for
RWCNet is available at
https://github.com/vigsivan/optimization-based-registration.
