Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans

06/14/2021 ∙ by Xinzi He, et al. ∙ 18

Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-established principle: recursive refinement of deformation vector fields across scales. We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields. RRN achieves state of the art performance for 3D registration of expiratory-inspiratory pairs of CT lung scans. On DirLab COPDGene dataset, RRN returns an average Target Registration Error (TRE) of 0.83 mm, which corresponds to a 13 from the best result presented in the leaderboard. In addition to comparison with conventional methods, RRN leads to 89 deep-learning-based peer approaches.



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