Koki Tomonaga
Papers
1
Total Citations
5
H-Index
1
About
Dr. Koki Tomonaga has established himself as a pioneering figure in the field of robotic-assisted laparoscopic surgery, with a focused expertise in deep learning for surgical image segmentation and autonomous camera control. His most cited work, a 2019 paper on a novel CNN architecture, tackles a critical challenge in minimally invasive surgery: eliminating the need for a human camera assistant. By introducing a recursive network structure designed to mitigate overfitting, Dr. Tomonaga’s approach enables more robust and accurate real-time segmentation of surgical scenes—a foundational step toward fully autonomous robotic laparoscope guidance. This contribution has garnered 5 citations, reflecting its growing influence among researchers developing intelligent surgical systems. His work directly addresses the practical bottleneck of camera control in laparoscopic procedures, where a surgeon currently relies on an assistant to hold and maneuver the scope. By advancing algorithms that allow a robot to autonomously track instruments and anatomy, Dr. Tomonaga is helping to streamline surgical workflows, reduce human error, and enhance operative efficiency. His research sits at the intersection of computer vision, robotics, and clinical surgery, marking him as a key innovator in the next generation of smart operating rooms.
Research Focus
Key Achievements
Top Papers
- 1