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.

翻译：大多数基于深度学习的医学图像配准算法主要聚焦于脑部图像配准任务。与脑部配准相比，胸部CT配准具有形变更大、背景更复杂以及区域重叠更显著的特点。本文提出一种快速无监督深度学习方法LDRNet，用于胸部CT图像的大形变图像配准。我们首先预测粗分辨率配准场，然后由粗到精逐步优化。我们提出了两项创新技术组件：1）用于在不同分辨率下优化配准场的细化模块；2）用于从高层特征学习变换矩阵的刚性模块。我们在私有数据集和公开数据集SegTHOR上训练并评估了模型。我们将本模型的性能与最先进的传统配准方法以及深度学习配准模型VoxelMorph、RCN和LapIRN进行了比较。结果表明，本模型在大形变图像配准中达到了最先进的性能，且速度显著更快。