Songe Choi

Papers

Total Citations

118

H-Index

About

Songe Choi is a leading researcher in computer vision for robotic surgery, with a focus on enhancing safety and precision in minimally invasive procedures. Her work centers on the automatic detection of surgical tools and critical events, such as hemorrhage, in laparoscopic robot-assisted surgery. Choi’s most influential contribution is her 2017 paper on “Surgical-tools detection based on Convolutional Neural Network in laparoscopic robot-assisted surgery,” which has garnered 108 citations. This work pioneered the use of CNNs to identify instruments in real-time, addressing a key challenge in robotic systems where undetected tools can cause tissue or organ damage. She further advanced the field with her 2016 study on “Automatic detection of hemorrhage and surgical instrument in laparoscopic surgery image,” which integrates CIELAB color space analysis and Otsu’s method for robust segmentation of bleeding areas and tools. Together, these contributions have laid the groundwork for smarter, safer surgical robots, reducing risks during operations. Choi’s research continues to shape the intersection of deep learning and medical imaging, offering practical solutions for real-time intraoperative monitoring.