Coupling Principled Refinement with Bi-Directional Deep Estimation for Robust Deformable 3D Medical Image Registration

Deformable 3D medical image registration is challenging due to the complicated transformations between image pairs. Traditional approaches estimate deformation fields by optimizing a task-guided energy embedded with physical priors, achieving high accuracy while suffering from expensive computational loads for the iterative optimization. Recently, deep networks, encoding the information underlying data examples, render fast predictions but severely dependent on training data and have limited flexibility. In this study, we develop a paradigm integrating the principled prior into a bi-directional deep estimation process. Inheriting from the merits of both domain knowledge and deep representation, our approach achieves a more efficient and stable estimation of deformation fields than the state-of-the-art, especially when the testing pairs exhibit great variations with the training.