A review of deep learning-based three-dimensional medical image registration methods

Haonan Xiao, Xinzhi Teng, Chenyang Liu, Tian Li, Ge Ren, Ruijie Yang, Dinggang Shen, Jing Cai

Research output: Journal article publicationReview articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research. The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.

Original languageEnglish
Pages (from-to)4895-4916
Number of pages22
JournalQuantitative Imaging in Medicine and Surgery
Volume11
Issue number12
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Artificial intelligence
  • Deep learning (dl)
  • Image registration
  • Image-guided radiotherapy (igrt)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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