Deep Learning Based Medical Image Registration: A Review

被引：0
作者：
Ying S. [1 ]
Yang W. [1 ]
Du S. [2 ]
Shi J. [3 ]
机构：
[1] Department of Mathematics, College of Sciences, Shanghai University, Shanghai
[2] Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi'an Jiaotong University, Xi'an
[3] School of Communication and Information Engineering, Shanghai University, Shanghai
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Deep learning; Diffeomorphism; Displacement vector field; Image registration; Multi-scale regularization;
D O I：
10.16451/j.cnki.issn1003-6059.202104001
中图分类号：
学科分类号：
摘要：
Image registration is a key technology in the field of medical image processing and intelligent analysis. The real-time registration cannot be accomplished due to the high complexity and computational cost of traditional registration methods. With the development of deep learning, learning based image registration methods achieve remarkable results. In this paper, the medical image registration methods based on deep learning are systematically summarized and divided into three categories, including supervised learning, unsupervised learning and dual supervised learning. On this basis, the advantages and disadvantages for each category are discussed. Furthermore, the regularization methods proposed in recent years are emphatically discussed, especially based on diffeomorphism and multi-scale regularization. Finally, the medical image registration methods based on deep learning are prospected according to the development trend of the current medical image registration methods. © 2021, Science Press. All right reserved.
引用
收藏
页码：287 / 299
页数：12
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