2021/08/04 by Mathias Unberath, Unberath, Mathias, Gao, Cong +10 · 5 citations
Medicine · #Colorectal Cancer Surgical Treatments #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Medical Physics (physics.med-ph) #Pancreatic and Hepatic Oncology Research #Robotics (cs.RO) #Surgical Simulation and Training
paper · pdf · doi:10.48550/arxiv.2108.02238
openalex publication_date 2021/08/04 · openalex created_date 2022/09/14 · openalex updated_date 2026/07/28
Image-based navigation is widely considered the next frontier of minimally\ninvasive surgery. It is believed that image-based navigation will increase the\naccess to reproducible, safe, and high-precision surgery as it may then be\nperformed at acceptable costs and effort. This is because image-based\ntechniques avoid the need of specialized equipment and seamlessly integrate\nwith contemporary workflows. Further, it is expected that image-based\nnavigation will play a major role in enabling mixed reality environments and\nautonomous, robotic workflows. A critical component of image guidance is 2D/3D\nregistration, a technique to estimate the spatial relationships between 3D\nstructures, e.g., volumetric imagery or tool models, and 2D images thereof,\nsuch as fluoroscopy or endoscopy. While image-based 2D/3D registration is a\nmature technique, its transition from the bench to the bedside has been\nrestrained by well-known challenges, including brittleness of the optimization\nobjective, hyperparameter selection, and initialization, difficulties around\ninconsistencies or multiple objects, and limited single-view performance. One\nreason these challenges persist today is that analytical solutions are likely\ninadequate considering the complexity, variability, and high-dimensionality of\ngeneric 2D/3D registration problems. The recent advent of machine\nlearning-based approaches to imaging problems that, rather than specifying the\ndesired functional mapping, approximate it using highly expressive parametric\nmodels holds promise for solving some of the notorious challenges in 2D/3D\nregistration. In this manuscript, we review the impact of machine learning on\n2D/3D registration to systematically summarize the recent advances made by\nintroduction of this novel technology. Grounded in these insights, we then\noffer our perspective on the most pressing needs, significant open problems,\nand possible next steps.\n