Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated issue to be tackled. On the other hand, the recently huge progress in the field of machine learning made by the possibility of implementing deep neural networks on the contemporary many-core GPUs opened up a promising window to challenge with many medical applications, where the registration is not an exception. In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented. The review is systematic and encompasses all the related works previously published in the field. Key concepts, statistical analysis from different points of view, confiding challenges, novelties and main contributions, key-enabling techniques, future directions and prospective trends all are discussed and surveyed in details in this comprehensive review. This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature.

中文翻译：

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使用深度神经网络的医学图像配准：综合回顾

图像引导干预正在挽救大量患者的生命，其中图像配准问题确实应该被视为最复杂和最复杂的问题。另一方面，最近在机器学习领域取得的巨大进步通过在当代多核 GPU 上实现深度神经网络的可能性打开了一个有希望的窗口来挑战许多医疗应用，其中注册也不例外. 在本文中，对使用深度神经网络进行医学图像配准的最新文献进行了全面回顾。该综述是系统的，涵盖了该领域以前发表的所有相关著作。关键概念、不同观点的统计分析、倾诉挑战、这篇综合评论中详细讨论和调查了新颖性和主要贡献、关键使能技术、未来方向和预期趋势。这篇综述为活跃在该领域的读者提供了深刻的理解和洞察力，他们正在研究最先进的技术并寻求为未来的文献做出贡献。