Learning features on robotic surgical tools

Computer-aided surgical interventions in both manual and robotic procedures have been shown to improve patient outcomes and enhance the skills of the human physician. Tool tracking is one such example that has various applications. In this paper, we show how to learn fine-scaled features on surgical tools for the purpose of pose estimation. Our experiments analyze different state-of-the-art feature descriptors coupled with various learning algorithms on in-vivo data from a surgical robot. We propose that it is important to be able to detect naturally-occurring features robustly in order to achieve long-term, marker-less tool tracking. We also contribute a new improvement on feature classification based on Randomized Trees.