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Image-based navigation is widely considered the next frontier of minimallyinvasive surgery. It is believed that image-based navigation will increase theaccess to reproducible, safe, and high-precision surgery as it may then beperformed at acceptable costs and effort. This is because image-basedtechniques avoid the need of specialized equipment and seamlessly integratewith contemporary workflows. Further, it is expected that image-basednavigation will play a major role in enabling mixed reality environments andautonomous, robotic workflows. A critical component of image guidance is 2D/3Dregistration, a technique to estimate the spatial relationships between 3Dstructures, e.g., volumetric imagery or tool models, and 2D images thereof,such as fluoroscopy or endoscopy. While image-based 2D/3D registration is amature technique, its transition from the bench to the bedside has beenrestrained by well-known challenges, including brittleness of the optimizationobjective, hyperparameter selection, and initialization, difficulties aroundinconsistencies or multiple objects, and limited single-view performance. Onereason these challenges persist today is that analytical solutions are likelyinadequate considering the complexity, variability, and high-dimensionality ofgeneric 2D/3D registration problems. The recent advent of machinelearning-based approaches to imaging problems that, rather than specifying thedesired functional mapping, approximate it using highly expressive parametricmodels holds promise for solving some of the notorious challenges in 2D/3Dregistration. In this manuscript, we review the impact of machine learning on2D/3D registration to systematically summarize the recent advances made byintroduction of this novel technology. Grounded in these insights, we thenoffer our perspective on the most pressing needs, significant open problems,and possible next steps.

The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective