Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.
Phys Med Biol. 2020 Oct 22;65(20):20TR01. doi: 10.1088/1361-6560/ab843e.
This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.
这篇论文综述了基于深度学习（DL）的医学图像配准方法。我们总结了基于 DL 的配准方法在医学领域的最新进展和应用。这些方法根据其方法、功能和流行程度分为七类。对每一类进行了详细的回顾，突出了重要的贡献，并确定了具体的挑战。在对每一类进行详细回顾之后，进行了简短的评估，以总结其成就和未来潜力。我们使用基准数据集对基于 DL 的肺部和脑部配准方法进行了全面比较。最后，我们从各个方面分析了所有引用文献的统计数据，揭示了基于 DL 的医学图像配准的流行趋势和未来趋势。
