[论文解读] Deep Learning for Medical Image Registration: A Comprehensive Review

Subrato Bharati, M. Rubaiyat Hossain Mondal+2 more|arXiv (Cornell University)|Apr 24, 2022

Radiomics and Machine Learning in Medical Imaging被引用 22

一句话总结

本论文综述基于深度学习的医学图像配准，涵盖监督、无监督、基于GAN的，以及深度迭代方法，应用于单模态和多模态成像，并讨论挑战与未来方向。

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.

研究动机与目标

• 推动研究基于深度学习的医学图像配准并对现有方法进行分类。
• 总结监督、双重监督和弱监督的配准范式。
• 评述基于相似性、基于GAN和深度迭代的配准方法。
• 讨论数据挑战及多样化成像模态（X线、CT、超声、MRI）。
• 突出该领域的未来研究方向。

提出的方法

• 将配准方法分类为监督、弱监督和无监督三类。
• 描述在深度学习中使用的基于相似性和基于GAN的配准方法。
• 解释深度迭代配准，重点放在深度相似性方法与强化学习策略上。
• 综述单模态和多模态配准的应用领域与成像模态。
• 指出关键挑战，如缺乏具有已知变换的数据集。

实验结果

研究问题

• RQ1 基于深度学习的医学图像配准的主要类别及其特征有哪些？
• RQ2 在DL中，监督、弱监督和无监督的配准方法有何比较？
• RQ3 基于相似性、基于GAN和深度迭代方法在配准中的作用是什么？
• RQ4 基于深度学习的配准的主要应用领域和成像模态有哪些？
• RQ5 该领域的主要挑战和未来方向是什么？

主要发现

• 综述涵盖监督、双重监督和弱监督的配准框架。
• 通过基于相似性和基于GAN的策略讨论无监督配准。
• 描述深度迭代配准，强调深度相似性方法和强化学习方法。
• 应用覆盖单模态和多模态成像，包括X线、CT、超声和MRI。
• 一个主要挑战是缺乏具有已知真实变换的训练数据。
• 本文概述了基于深度学习的医学图像配准的有前景的未来研究方向。