  Deformable image registration is fundamental to longitudinal and population
analysis. Geometric alignment of the infant brain MR images is challenging,
owing to rapid changes in image appearance in association with brain
development. In this paper, we propose an infant-dedicated deep registration
network that uses the auto-context strategy to gradually refine the deformation
fields to obtain highly accurate correspondences. Instead of training multiple
registration networks, our method estimates the deformation fields by invoking
a single network multiple times for iterative deformation refinement. The final
deformation field is obtained by the incremental composition of the deformation
fields. Experimental results in comparison with state-of-the-art registration
methods indicate that our method achieves higher accuracy while at the same
time preserves the smoothness of the deformation fields. Our implementation is
available online.
