基于深度学习的医学图像配准技术研究进展

医学图像配准技术对于病灶检测、临床诊断、手术规划，疗效评估等有着广泛的应用价值。系统性地总结了基于深度学习的配准算法，从深度迭代、全监督、弱监督到无监督学习的研究发展趋势，分析了各种方法的优势与局限。总体来看，无论是对数据的要求、配准精度，还是计算效率，无监督学习因其不依赖金标准和解剖标签，采用端到端的网络配准框架就可以自动执行需要的任务等优势成为研究的主流方向。然而，基于无监督学习的医学图像配准方法在医学图像领域的可解释性、跨模态多样性和可重复可扩展性方面同样面临着一些研究难点和挑战，这为将来实现更精准的医学图像配准方法指明了研究方向。

Research Progress of Medical Image Registration Technology Based on Deep Learning

Medical image registration technology has a wide range of application values for lesion detection, clinical diagnosis, surgical planning, and efficacy evaluation. This paper systematically summarizes the registration algorithm based on deep learning, and analyzes the advantages and limitations of various methods from deep iteration, full supervision, weak supervision to unsupervised learning. In general, unsupervised learning has become the mainstream direction of medical image registration research, because it does not rely on golden standards and uses an end-to-end network to save time. Meanwhile, compared with other methods, unsupervised learning can achieve higher accuracy and spends shorter time. However, medical image registration methods based on unsupervised learning also face some research difficulties and challenges in terms of interpretability, cross-modal diversity, and repeatable scalability in the field of medical images, which points out the research direction for achieving more accurate medical image registration methods in the future.