Papers
37
Total Citations
342
H-Index
10
About
Jeffrey H. Siewerdsen is a leading figure in medical imaging and image-guided surgery, with his work fundamentally advancing intraoperative cone-beam CT (CBCT) and surgical robotics. His research centers on developing novel imaging systems, task-driven optimization of source-detector trajectories, and automated algorithms for surgical planning and guidance. A major contribution is the creation of self-configuring deep learning networks for automated segmentation of complex anatomy, such as the temporal bone for neurotologic surgery, a method that has garnered significant attention. His pioneering work on automatic pedicle screw planning using atlas-based registration has been cited over 40 times, demonstrating its impact on spinal surgery. Siewerdsen has also made key advances in robotic drill guide positioning and image-guided transoral robotic surgery, aiming to improve precision and safety in minimally invasive procedures. His development of non-circular CBCT orbits for metal artifact reduction addresses a critical challenge in intraoperative imaging. With numerous highly cited papers, Siewerdsen's research is shaping the future of surgical navigation and robotic assistance, making him a pivotal figure in the field.
Research Focus
Key Achievements
Top Papers
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- 5Toward intraoperative image-guided transoral robotic surgery22 citations · 2013
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- 8Intraoperative image-guided transoral robotic surgery: pre-clinical studies18 citations · 2014
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