Deep Learning for Medical Image Registration: A Comprehensive Review

TL;DR

This comprehensive review explores deep learning methods for medical image registration, covering supervised, unsupervised, and reinforcement learning approaches, highlighting challenges and future research directions in the field.

Contribution

It provides an extensive overview of DL-based medical image registration techniques, categorizing methods and discussing their applications, challenges, and future prospects.

Findings

Deep learning models have advanced medical image registration significantly.

Supervised and unsupervised DL methods are both actively researched.

Challenges include lack of datasets with known transformations.

Abstract

Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervised registration categories, for example, fully supervised, dual supervised, and weakly supervised registration. Next, similarity-based as well as generative adversarial network (GAN)-based registration are presented as part of unsupervised registration. Deep iterative registration is then described with emphasis on deep similarity-based and reinforcement learning-based registration. Moreover, the application areas of medical image registration are reviewed. This review focuses on monomodal and multimodal registration and associated imaging, for instance, X-ray, CT scan, ultrasound, and MRI. The existing challenges are highlighted in this review, where it is shown that a major challenge is the absence of a training dataset with known transformations. Finally, a discussion is provided on the promising future research areas in the field of DL-based medical image registration.