# Deep Learning in Medical Image Registration: A Survey

## TL;DR

This survey reviews recent advances in deep learning methods for medical image registration, emphasizing research challenges, innovations, and future directions to improve clinical applications.

## Contribution

It provides a comprehensive overview of deep learning techniques in medical image registration, highlighting recent developments, challenges, and potential future research paths.

## Key findings

- Deep learning has achieved state-of-the-art results in medical image registration.
- The survey identifies key challenges and research gaps in current methods.
- Future directions include addressing data scarcity and model robustness.

## Abstract

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.