论文标题
训练多模式图像登记网络的单峰环循环正则化
Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks
论文作者
论文摘要
无监督的多模式图像登记框架的损耗函数有两个术语，即用于相似性度量和正则化的度量。在深度学习时代，研究人员提出了许多自动学习相似性指标的方法，该方法已显示出有效改善注册性能的方法。但是，对于正则化项，大多数现有的多模式注册方法仍然使用手工制作的公式将人工特性施加在估计的变形场上。在这项工作中，我们提出了一个单峰循环正则化训练管道，该管道从更简单的单峰登记中学习了特定于任务的先验知识，以限制多模式注册的变形场。在腹部CT-MR注册的实验中，该方法比常规正则化方法产生更好的结果，尤其是对于严重变形的局部区域。
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.
