IIRP-Net: Iterative Inference Residual Pyramid Network for Enhanced Image Registration

Deep learning-based image registration (DLIR) methods have achieved remarkable success in deformable image registration. We observe that iterative inference can exploit the well-trained registration network to the fullest extent. In this work, we propose a novel Iterative Inference Residual Pyramid Network (IIRP-Net) to enhance registration performance without any additional training costs. In IIRP-Net, we construct a streamlined pyramid registration network consisting of a feature extractor and residual flow estimators (RP-Net) to achieve generalized capabilities in feature extraction and registration. Then, in the inference phase, IIRP-Net employs an iterative inference strategy to enhance RP-Net by iteratively reutilizing residual flow estimators from coarse to fine. The number of iterations is adaptively determined by the proposed IterStop mechanism. We conduct extensive experiments on the FLARE and Mindboggle datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art deformable image registration methods. Our code is available at https://github.com/Torbjorn1997/IIRP-Net.