Zou Maoyang, Yang Hao, Pan Guanghui, Zhong Yong
Chengdu University of Information Technology, Chengdu 610225, P.R.China;Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, Chengdu 610041, P.R.China.
Chengdu University of Information Technology, Chengdu 610225, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2019 Aug 25;36(4):677-683. doi: 10.7507/1001-5515.201810004.
With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.
随着图像引导手术和放射治疗的发展，对医学图像配准的需求更加强烈，挑战也更大。近年来，深度学习，尤其是深度卷积神经网络，在医学图像处理方面取得了优异成果，其在配准方面的研究发展迅速。本文根据技术方法类别综述了国内外基于深度学习的医学图像配准研究进展，这些技术方法包括采用迭代优化策略的相似性度量、变换参数的直接估计等。然后，分析了深度学习在医学图像配准中的挑战，并提出了可能的解决方案和开放研究方向。
