Deep learning in medical image registration: a review.

Journal: Physics in medicine and biology
Published Date:

Abstract

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.

Authors

  • Yabo Fu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Yang Lei
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Tonghe Wang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Walter J Curran
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.