SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
Accurate instrument pose estimation is a crucial step towards the future of
robotic surgery, enabling applications such as autonomous surgical task
execution. Vision-based methods for surgical instrument pose estimation provide
a practical approach to tool tracking, but they often require markers to be
attached to the instruments. Recently, more research has focused on the
development of marker-less methods based on deep learning. However, acquiring
realistic surgical data, with ground truth instrument poses, required for deep
learning training, is challenging. To address the issues in surgical instrument
pose estimation, we introduce the Surgical Robot Instrument Pose Estimation
(SurgRIPE) challenge, hosted at the 26th International Conference on Medical
Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The
objectives of this challenge are: (1) to provide the surgical vision community
with realistic surgical video data paired with ground truth instrument poses,
and (2) to establish a benchmark for evaluating markerless pose estimation
methods. The challenge led to the development of several novel algorithms that
showcased improved accuracy and robustness over existing methods. The
performance evaluation study on the SurgRIPE dataset highlights the potential
of these advanced algorithms to be integrated into robotic surgery systems,
paving the way for more precise and autonomous surgical procedures. The
SurgRIPE challenge has successfully established a new benchmark for the field,
encouraging further research and development in surgical robot instrument pose
estimation.
