The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.

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医学图像配准中的深度学习：一项调查

通过强大的图像配准建立图像对应关系对于许多临床任务（例如图像融合，器官图谱创建和肿瘤生长监测）至关重要，这是一个非常具有挑战性的问题。自从最近的深度学习复兴开始以来，医学影像研究界已经开发了基于深度学习的方法，并在包括图像配准在内的许多应用中实现了最先进的技术。在过去的几年中，深度学习在图像配准应用中的迅速采用，需要全面的总结和展望，这是本次调查的主要范围。这要求将重点放在不同的研究领域上，并强调从业人员面临的挑战。因此，这项调查 概述了近年来在研究挑战和相关创新的背景下基于深度学习的医学图像配准的发展。此外，本调查重点介绍了未来的研究方向，以显示该领域可能如何向前发展。