Slice-to-volume medical image registration: a survey
arXiv:1702.01636 · doi:10.1016/j.media.2017.04.010
Abstract
During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.
Accepted for publication in Medical Image Analysis
References in corpus (7)
- Fully Convolutional Networks for Semantic Segmentation
- Loopy Belief Propagation for Approximate Inference: An Empirical Study
- FlowNet: Learning Optical Flow with Convolutional Networks
- Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
- Learning to Compare Image Patches via Convolutional Neural Networks
- Do Convnets Learn Correspondence?
- A Deep Metric for Multimodal Registration
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