Abstract：
We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.
摘要：
我们提出了一种用于无监督3D脑图像配准的双流金字塔配准网络（简称为Dual-PRNet）。与最近基于CNN的注册方法不同，比如VoxelMorph,使用单流网络从一对3D体积计算注册字段，我们设计了一个两流体系结构，能够从一对特征金字塔顺序估计多级注册字段。我们的主要贡献是：（i）我们设计了一个两流3D编码器-解码器网络，该网络可以分别从两个输入体积计算两个卷积特征金字塔；（ii）我们提出了顺序金字塔注册，其中一系列金字塔注册（PR）模块被设计为直接从解码特征金字塔预测多级注册字段。注册字段通过顺序扭曲以粗到细的方式逐渐细化，使模型具有处理大变形的强大能力；（iii）可以通过计算特征金字塔之间的局部3D相关性来进一步增强PR模块，导致改进的Dual-PRNet++能够聚合大脑丰富的详细解剖结构;（iv）我们的Dual-PRNet++可以集成到3D分割框架中，用于联合配准和分割，通过精确扭曲体素级别的注释。我们的方法是在大脑MRI配准的两个标准基准上进行评估的，双PRNet++在很大程度上优于最先进的方法，即，将Mindboggle101数据集上最近的VoxelMorph从0.511提高到0.748（Dice评分）。此外,我们进一步证明了我们的方法可以极大地促进联合学习框架中的细分任务，通过利用有限的注释。
