Ioannis Gkouzionis
Papers
1
Total Citations
20
H-Index
1
About
Ioannis Gkouzionis is a researcher at the forefront of applying self-supervised learning to medical imaging, with a primary focus on depth estimation in laparoscopic surgery. His most cited work, "Self-supervised Depth Estimation in Laparoscopic Image Using 3D Geometric Consistency" (2022), has garnered 20 citations and introduces a novel approach that leverages 3D geometric consistency to train depth estimation models without requiring ground-truth depth data. This contribution is pivotal for enhancing 3D scene understanding in minimally invasive procedures, enabling more accurate spatial awareness for surgical navigation and augmented reality guidance. By eliminating the need for costly labeled datasets, Gkouzionis’s method paves the way for scalable, real-time depth perception in clinical settings. His research sits at the intersection of computer vision, deep learning, and surgical robotics, addressing critical challenges in intraoperative imaging. With a growing citation impact, Gkouzionis is establishing himself as a key contributor to the advancement of autonomous surgical systems, where robust depth estimation is essential for improving patient outcomes and surgical precision.
Research Focus
Key Achievements
Top Papers
- 1