提出快速无监督的胸部CT大形变配准方法，提升精度与速度。
LDRNet: Large Deformation Registration Model for Chest CT Registration
- 分粗到细迭代优化配准场，结合精修与刚性模块。
- 在SegTHOR和私有数据集上达到最优配准精度，速度显著更快。
- 适合需要高精度、快速处理胸部CT的大形变配准场景。
大多数基于深度学习的医学图像配准算法聚焦于脑部图像。相比脑部配准，胸部CT配准面临更大形变、更复杂的背景及区域重叠问题。本文提出一种快速无监督深度学习方法LDRNet，用于胸部CT的大形变图像配准。首先预测粗分辨率配准场，再从粗到细逐步精修。提出两个创新组件：1）多分辨率精修块，用于不同尺度下优化配准场；2）刚性块，从高层特征中学习变换矩阵。模型在私有数据集和公开数据集SegTHOR上进行训练与评估，并与先进传统方法及深度学习模型VoxelMorph、RCN、LapIRN对比。结果表明，该模型在大形变图像配准任务中达到当前最佳性能，且速度大幅提升。
原文摘要 · Abstract (English)
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.
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