  Convolutional neural networks (CNNs) have been widely used to build deep
learning models for medical image registration, but manually designed network
architectures are not necessarily optimal. This paper presents a hierarchical
NAS framework (HNAS-Reg), consisting of both convolutional operation search and
network topology search, to identify the optimal network architecture for
deformable medical image registration. To mitigate the computational overhead
and memory constraints, a partial channel strategy is utilized without losing
optimization quality. Experiments on three datasets, consisting of 636
T1-weighted magnetic resonance images (MRIs), have demonstrated that the
proposal method can build a deep learning model with improved image
registration accuracy and reduced model size, compared with state-of-the-art
image registration approaches, including one representative traditional
approach and two unsupervised learning-based approaches.
