Abstract
Robot-assisted surgery (RAS), a type of minimally invasive surgery, is used in a variety of clinical surgeries because it has a faster recovery rate and causes less pain. Automatic video analysis of RAS is an active research area, where precise surgical tool detection in real time is an important step. However, most deep learning methods currently employed for surgical tool detection are based on anchor boxes, which results in low detection speeds. In this paper, we propose an anchor-free convolutional neural network (CNN) architecture, a novel frame-by-frame method using a compact stacked hourglass network, which models the surgical tool as a single point: the center point of its bounding box. Our detector eliminates the need to design a set of anchor boxes, and is end-to-end differentiable, simpler, more accurate, and more efficient than anchor-box-based detectors. We believe our method is the first to incorporate the anchor-free idea for surgical tool detection in RAS videos. Experimental results show that our method achieves 98.5% mAP and 100% mAP at 37.0 fps on the ATLAS Dione and Endovis Challenge datasets, respectively, and truly realizes real-time surgical tool detection in RAS videos.
Highlights
Robot-assisted surgery (RAS) is the latest development in minimally invasive surgical technology
We introduce a compact stacked hourglass network [29] to detect the surgical tool as the center point of its bounding box
We present the mean average precision at intersection over union (IoU) threshold 0.5
Summary
Robot-assisted surgery (RAS) is the latest development in minimally invasive surgical technology. Robotic surgical tools make it easy to perform complex motion tasks during surgery by transforming the surgeon’s real-time hand movements and forces acting on the tissue into small-scale movements [1]. Despite its advantages in minimally invasive surgery, the RAS system still has problems, such as a narrow field of view, narrow operating space, and insufficient tactile feedback, which may cause holes in organs and tissues during an operation [2]. To have real-time information on the motions of a surgical tool can help model poses for real-time automated surgical video analysis [3]–[6], which assists surgeons with automatic report generation, optimized. In this study, we focus on real-time surgical tool detection in videos
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