Deep learning in medical image registration: a review

被引:345
|
作者
Fu, Yabo [1 ]
Lei, Yang [1 ]
Wang, Tonghe [1 ,2 ]
Curran, Walter J. [1 ,2 ]
Liu, Tian [1 ,2 ]
Yang, Xiaofeng [1 ,2 ]
机构
[1] Emory Univ, Dept Radiat Oncol, Atlanta, GA 30322 USA
[2] Emory Univ, Winship Canc Inst, Atlanta, GA 30322 USA
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2020年 / 65卷 / 20期
基金
美国国家卫生研究院;
关键词
deep learning; medical image registration; review; DEFORMABLE REGISTRATION; DOSE ACCUMULATION; NEURAL-NETWORKS; FRAMEWORK; RECONSTRUCTION; MOTION; ACCURACY; LOCALIZATION; SEGMENTATION; MODEL;
D O I
10.1088/1361-6560/ab843e
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页数:27
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