Deep learning in medical image registration: a review

By Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang. Physics in Medicine and Biology, 2019

620 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2cc8989238746edadcab9762e008ca6b66eb9288

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A review on deep learning in medical image analysis (2021, methodology): “Different kinds of architectures are used for wide variety of applications that is mentioned in Table 1 [11].”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “4) Medical image registration [16], which aims to align the spatial coordinates of one or more images into a common coordinate system.”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “Image registration can be sorted into two groups: rigid and deformable (non-rigid) (Fu et al., 2020).”
A review of multimodal image matching: Methods and applications (2021, background): “Others typically review implementations [37,38] or deep learning frameworks [28,30].”