bibtext: @InProceedings{ Par_Towards_MICCAI2024,
                   author = { Park, Jihun and Hong, Jiuk and Yoon, Jihun and Park, Bokyung and Choi, Min-Kook and Jung, Heechul },
                   title = { { Towards Precise Pose Estimation in Robotic Surgery: Introducing Occlusion-Aware Loss } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Towards Precise Pose Estimation in Robotic Surgery: Introducing Occlusion-Aware Loss
authors: Park, Jihun Hong, Jiuk Yoon, Jihun Park, Bokyung Choi, Min-Kook Jung, Heechul