Lukas Zingg
Papers
1
Total Citations
10
H-Index
1
About
Lukas Zingg is a researcher at the forefront of surgical navigation and medical imaging, with a primary focus on 3D reconstruction and domain adaptation for orthopedic applications. His most-cited work, "Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data" (2024, 10 citations), addresses critical barriers to the clinical adoption of surgical navigation—namely time constraints, cost, radiation exposure, and workflow integration. Building on the foundation of his earlier X23D framework, Zingg develops methods that bridge the gap between synthetic training data and real-world fluoroscopy, enabling accurate, real-time 3D spine reconstruction without excessive computational overhead. This contribution is pivotal for making intraoperative navigation more accessible and practical in routine orthopedic surgeries. By tackling the domain shift problem in medical imaging, Zingg’s work directly impacts patient outcomes and surgical precision. His research is highly relevant for students and engineers interested in the intersection of computer vision, deep learning, and clinical translation, demonstrating how algorithmic innovation can solve real-world surgical challenges.
Research Focus
Key Achievements
Top Papers
- 1