Slice-to-volume medical image registration: a survey

By Enzo Ferrante, Nikos Paragios. 2017

181 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, background): “IMAGE registration is one of the most fundamental tools in biomedical image processing, with applications that range from image-based navigation in imaging and image-guided interventions to longitudinal and group analyses [1]–[7].”
A review of multimodal image matching: Methods and applications (2021, background): “Most works focus on medical image registration to deliver specific instruction, either aiming at 3D– 2D registration [31,32], deformable registration [29], or registration of different objects such as breast [33,34], brain [35], and vascular [36].”
Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “Please refer to Zitova and Flusser (2003), Dawn et al. (2010), Sotiras et al. (2013) and Ferrante and Paragios (2017) for representative literature and further details.”
Unsupervised End-to-end Learning for Deformable Medical Image Registration (2017, background): “For such tasks, ROI segmentation is a prerequisite for successful image registration as shown in [48], [50], [51], as it can focus the registration process on specific regions of interest (ROI) and avoid the undesired alignment of artifacts.”