深層学習による術野知覚：ロボット組織操作のための手術知覚フレームワーク
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction
深層学習で特徴抽出を行うことで、手術道具の追跡と変形組織のマッピングを高精度に行う手術知覚フレームワークを提案し、公開データセットで最先端の追跡性能を示した。
著者: Jingpei Lu, Ambareesh Jayakumari, Florian Richter, Yang Li, Michael C. Yip
分類: cs.RO, cs.CV
原文アブストラクト
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.
