Deep Learning in Medical Image Registration: A Review

TL;DR

This review comprehensively summarizes recent deep learning approaches for medical image registration, categorizing methods, analyzing their achievements, challenges, and future trends, with benchmark comparisons for lung and brain registration.

Contribution

It provides a systematic classification, detailed review, and comparative analysis of DL-based medical image registration methods, highlighting current progress and future directions.

Findings

DL methods show promising accuracy in lung and brain registration

Benchmark datasets reveal trends and performance gaps

Deep learning techniques are increasingly popular in medical image registration

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 potentials. We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.