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

HNAS-reg: hierarchical neural architecture search for deformable medical image registration