Progressive 3D biomedical image registration network based on deep self-calibration - Sun, Rui; Wu, Jun; Miao, Yongchun; Ouyang, Lei; Qu, Lei (Frontiers in Neuroinformatics, 2022)
3D deformable image registration (DIR) is a key enabling techniques in building digital neuronal atlases of brain, which can model the local nonlinear deformation between a pair of biomedical images and align the anatomical structures of different samples into one spatial coordinate system. And thus, the DIR is always conducted following a preprocessing of global linear registration to remove the large global deformations. However, imperfect preprocessing may leave some large nonlinear deformations that cannot be handled well by existing DIR methods. The recently proposed cascaded registration network gives a primary solution to deal with such large nonlinear deformations, but still suffers from loss of image details caused by continuous interpolation (information loss problem). In this paper, a progressive image registration strategy based on deep self-calibration is proposed to deal with the large nonlinear deformations without causing information loss and introducing additional parameters. More importantly, we also propose a novel hierarchical registration strategy to quickly achieve accurate multi-scale progressive registration. In addition, our method can implicitly and reasonably implement dynamic dataset augmentation. We have evaluated the proposed method on both optical and MRI images datasets with obtaining promising results, which demonstrate the superior performance of the proposed method over several other state-of-the-art approaches for deformable image registration.