用未训练的神经网络实现多模态图像配准，无需更换模型或目标函数。
Multi-modal deformable image registration using untrained neural networks
- 用容量有限的未训练网络作为隐式先验引导配准
- 在单模态与多模态、刚性与非刚性场景中均表现良好
- 适用于多种图像类型，无需调整模型结构
图像配准方法通常假设待配准图像具有特定类型（如单模态与多模态、2D与3D、刚性与非刚性），缺乏能适应所有条件的通用方法。本文提出一种利用神经网络进行图像表征的配准方法。该方法使用容量受限的未训练网络作为隐式先验，以指导获得良好配准结果。与以往针对特定数据类型的专用方法不同，本方法可同时处理刚性与非刚性、单模态与多模态配准，且无需更改模型或目标函数。我们在多种数据集上进行了全面评估，验证了其在不同条件下的优异性能。
原文摘要 · Abstract (English)
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.
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