Xu Zhe, Yan Jiangpeng, Luo Jie, Wells William, Li Xiu, Jagadeesan Jayender
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Proc IEEE Int Symp Biomed Imaging. 2021 Apr;2021. doi: 10.1109/isbi48211.2021.9433926. Epub 2021 May 25.
The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration, to constrain the deformation field of multimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods, especially for severely deformed local regions.
无监督多模态图像配准框架的损失函数有两项，即用于相似性度量的指标和正则化项。在深度学习时代，研究人员提出了许多自动学习相似性度量的方法，这些方法已被证明在提高配准性能方面是有效的。然而，对于正则化项，大多数现有的多模态配准方法仍然使用手工公式对估计的变形场施加人为属性。在这项工作中，我们提出了一种单模态循环正则化训练管道，它从更简单的单模态配准中学习特定任务的先验知识，以约束多模态配准的变形场。在腹部CT-MR配准实验中，所提出的方法比传统正则化方法产生了更好的结果，特别是对于严重变形的局部区域。
