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Deep learning technologies have dramatically reshaped the field of medicalimage registration over the past decade. The initial developments, such asResNet-based and U-Net-based networks, established the foundation for deeplearning in image registration. Subsequent progress has been made in variousaspects of deep learning-based registration, including similarity measures,deformation regularizations, and uncertainty estimation. These advancementshave not only enriched the field of image registration but have alsofacilitated its application in a wide range of tasks, including atlasconstruction, multi-atlas segmentation, motion estimation, and 2D-3Dregistration. In this paper, we present a comprehensive overview of the mostrecent advancements in deep learning-based image registration. We begin with aconcise introduction to the core concepts of deep learning-based imageregistration. Then, we delve into innovative network architectures, lossfunctions specific to registration, and methods for estimating registrationuncertainty. Additionally, this paper explores appropriate evaluation metricsfor assessing the performance of deep learning models in registration tasks.Finally, we highlight the practical applications of these novel techniques inmedical imaging and discuss the future prospects of deep learning-based imageregistration.

A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond