论文标题
用拉普拉斯金字塔网络的大变形差异图像登记
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
论文作者
论文摘要
基于深度学习的方法最近证明了针对广泛的医学图像分析任务的可变形图像注册的有希望的结果。但是，现有的基于深度学习的方法通常仅限于小型变形设置，并且这些方法通常会忽略转换的理想特性，包括指测和拓扑保存。在本文中，我们提出了一个深层的拉普拉斯金字塔图像登记网络，该网络可以在差异图的空间内以粗到更精细的方式解决图像登记优化问题。对两个MR脑扫描数据集进行的广泛定量和定性评估表明，我们的方法以显着的边距优于现有方法，同时保持理想的差异性能和有希望的注册速度。
Deep learning-based methods have recently demonstrated promising results in deformable image registration for a wide range of medical image analysis tasks. However, existing deep learning-based methods are usually limited to small deformation settings, and desirable properties of the transformation including bijective mapping and topology preservation are often being ignored by these approaches. In this paper, we propose a deep Laplacian Pyramid Image Registration Network, which can solve the image registration optimization problem in a coarse-to-fine fashion within the space of diffeomorphic maps. Extensive quantitative and qualitative evaluations on two MR brain scan datasets show that our method outperforms the existing methods by a significant margin while maintaining desirable diffeomorphic properties and promising registration speed.
