标题： 使用未训练神经网络的多模态可变形图像配准 显示英文标题

标题： Multi-modal deformable image registration using untrained neural networks

摘要： 图像配准技术通常假设要配准的图像是某种类型（例如，单模态与多模态、2D 与 3D、刚性与可变形），并且缺乏一种可以在所有条件下处理数据的通用方法。我们提出了一种利用神经网络进行图像表示的配准方法。我们的方法使用具有有限表示能力的未训练网络作为隐式先验，以引导良好的配准。与之前专门针对特定数据类型的方案不同，我们的方法能够处理刚性和非刚性以及单模态和多模态配准，而无需对模型或目标函数进行更改。我们通过多种数据集进行了全面的评估研究，并展示了有前景的性能。 显示英文摘要

摘要： Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propose a registration method that utilizes neural networks for image representation. Our method uses untrained networks with limited representation capacity as an implicit prior to guide for a good registration. Unlike previous approaches that are specialized for specific data types, our method handles both rigid and non-rigid, as well as single- and multi-modal registration, without requiring changes to the model or objective function. We have performed a comprehensive evaluation study using a variety of datasets and demonstrated promising performance.