Research progress and challenges of deep learning in medical image registration

被引：4

作者：

Zou, Maoyang ^{[1
,2
]}

Yang, Hao ^{[1
]}

Pan, Guanghui ^{[1
]}

Zhong, Yong ^{[2
]}

机构：

[1] Chengdu University of Information Technology, Chengdu,610225, China

[2] Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, Chengdu,610041, China

来源：

Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering | 2019年 / 36卷 / 04期

关键词：

Image registration - Iterative methods - Deep neural networks - Medical imaging;

D O I：

10.7507/1001-5515.201810004

中图分类号：

学科分类号：

摘要：

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. Copyright © 2019 by Editorial Office of Journal of Biomedical Engineering.

引用

页码：677 / 683