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Robotic automation in surgery requires precise tracking of surgical tools andmapping of deformable tissue. Previous works on surgical perception frameworksrequire significant effort in developing features for surgical tool and tissuetracking. In this work, we overcome the challenge by exploiting deep learningmethods for surgical perception. We integrated deep neural networks, capable ofefficient feature extraction, into the tissue reconstruction and instrumentpose estimation processes. By leveraging transfer learning, the deep learningbased approach requires minimal training data and reduced feature engineeringefforts to fully perceive a surgical scene. The framework was tested on threepublicly available datasets, which use the da Vinci Surgical System, forcomprehensive analysis. Experimental results show that our framework achievesstate-of-the-art tracking performance in a surgical environment by utilizingdeep learning for feature extraction.

SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction