Towards Precise Pose Estimation in Robotic Surgery: Introducing Occlusion-Aware Loss

Jihun Park, Jiuk Hong, Jihun Yoon, Bokyung Park, Min-Kook Choi, Heechul Jung

Abstract: Accurate pose estimation of surgical instruments is crucial for analyzing robotic surgery videos using computer vision techniques. However, the scarcity of suitable public datasets poses a challenge in this regard. To address this issue, we have developed a new private dataset extracted from real gastric cancer surgery videos. The primary objective of our research is to develop a more sophisticated pose estimation algorithm for surgical instruments using this private dataset. Additionally, we introduce a novel loss function aimed at enhancing the accuracy of pose estimation, with a specific emphasis on minimizing root mean squared error. Leveraging the YOLOv8 model, our approach significantly outperforms existing methods and state-of-the-art techniques, thanks to the enhanced occlusion-aware loss function. These findings hold promise for improving the precision and safety of robotic-assisted surgeries.

External IDs:dblp:conf/miccai/ParkHYPCJ24