A review of deep learning-based deformable medical image registration - Zou, Jing; Gao, Bingchen; Song, Youyi; Qin, Jing (Frontiers in Oncology, 2022)
The alignment of images through deformable image registration is vital to clinical applications (e.g. atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into 5 categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about the contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publically available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.