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A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond
Over the past decade, deep learning technologies have greatly advanced the
field of medical image registration. The initial developments, such as
ResNet-based and U-Net-based networks, laid the groundwork for deep
learning-driven image registration. Subsequent progress has been made in
various aspects of deep learning-based registration, including similarity
measures, deformation regularizations, and uncertainty estimation. These
advancements have not only enriched the field of deformable image registration
but have also facilitated its application in a wide range of tasks, including
atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D
registration. In this paper, we present a comprehensive overview of the most
recent advancements in deep learning-based image registration. We begin with a
concise introduction to the core concepts of deep learning-based image
registration. Then, we delve into innovative network architectures, loss
functions specific to registration, and methods for estimating registration
uncertainty. Additionally, this paper explores appropriate evaluation metrics
for assessing the performance of deep learning models in registration tasks.
Finally, we highlight the practical applications of these novel techniques in
medical imaging and discuss the future prospects of deep learning-based image
registration
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