LDRNet: Large Deformation Registration Model for Chest CT Registration
This addresses the problem of accurate and efficient medical image registration for chest CT scans, which is incremental as it builds on existing deep learning methods with specific technical improvements.
The paper tackles large deformation chest CT image registration, which is more challenging than brain registration due to larger deformations and complex backgrounds, and proposes LDRNet, an unsupervised deep learning method that achieves state-of-the-art performance and is much faster than existing methods.
Most of the deep learning based medical image registration algorithms focus on brain image registration tasks.Compared with brain registration, the chest CT registration has larger deformation, more complex background and region over-lap. In this paper, we propose a fast unsupervised deep learning method, LDRNet, for large deformation image registration of chest CT images. We first predict a coarse resolution registration field, then refine it from coarse to fine. We propose two innovative technical components: 1) a refine block that is used to refine the registration field in different resolutions, 2) a rigid block that is used to learn transformation matrix from high-level features. We train and evaluate our model on the private dataset and public dataset SegTHOR. We compare our performance with state-of-the-art traditional registration methods as well as deep learning registration models VoxelMorph, RCN, and LapIRN. The results demonstrate that our model achieves state-of-the-art performance for large deformation images registration and is much faster.