Deep Learning for Medical Image Registration: A Comprehensive Review

• URL: http://arxiv.org/abs/2204.11341v1
• Date: Sun, 24 Apr 2022 19:34:00 GMT
• Title: Deep Learning for Medical Image Registration: A Comprehensive Review
• Authors: Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder, V. B. Surya Prasath
• Abstract summary: There has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This review focuses on monomodal and multimodal registration and associated imaging, for instance, X-ray, CT scan, ultrasound, and MRI.
• Score: 1.3190581566723918
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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