Bareum Choi

Papers

Total Citations

118

H-Index

About

Bareum Choi is a leading researcher in surgical robotics and computer vision, with a focus on enhancing the safety and precision of minimally invasive procedures. His work centers on the automatic detection of surgical tools and critical events, such as hemorrhage, in laparoscopic robot-assisted surgery. Choi’s most impactful contribution is his pioneering 2017 paper on "Surgical-tools detection based on Convolutional Neural Network in laparoscopic robot-assisted surgery," which has garnered over 108 citations. This work introduced a deep learning framework to identify surgical instruments in real-time, addressing a key challenge in preventing accidental tissue damage during robotic procedures. He further advanced the field with his 2016 study on "Automatic detection of hemorrhage and surgical instrument in laparoscopic surgery image," which combined CIELAB color space analysis with Otsu’s method to segment bleeding regions and tools simultaneously. By integrating AI-driven visual intelligence into surgical workflows, Choi’s research has laid the groundwork for safer, more autonomous robotic surgery systems, making him a notable figure in the intersection of medical imaging and machine learning.