SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction
This work addresses the problem of reducing feature engineering efforts for surgical perception in robotic automation, though it appears incremental as it applies existing deep learning methods to this domain.
The authors tackled the challenge of precise surgical tool tracking and deformable tissue mapping in robotic surgery by integrating deep neural networks for feature extraction, achieving state-of-the-art tracking performance on three da Vinci Surgical System datasets.
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue. Previous works on surgical perception frameworks require significant effort in developing features for surgical tool and tissue tracking. In this work, we overcome the challenge by exploiting deep learning methods for surgical perception. We integrated deep neural networks, capable of efficient feature extraction, into the tissue reconstruction and instrument pose estimation processes. By leveraging transfer learning, the deep learning based approach requires minimal training data and reduced feature engineering efforts to fully perceive a surgical scene. The framework was tested on three publicly available datasets, which use the da Vinci Surgical System, for comprehensive analysis. Experimental results show that our framework achieves state-of-the-art tracking performance in a surgical environment by utilizing deep learning for feature extraction.