Feasibility of Online Semantic Labeling of Deformable Tissues for Minimally Invasive Surgery1

Abstract

Vascular injury accounts for one third of complications in laparoscopy [1]. It is the second highest cause of death within laparoscopic surgery, second only to anesthesia, with a mortality rate estimated at 15% [2]. Surgery is also extremely common, currently surgeries are performed at a rate of 50 million per year, giving the average American an expected seven surgeries in their lifetime [3]. There is currently no way to (1) detect imminent vascular injury during surgery (2) prevent it via alarm or possibly an automated emergency stop in the case of a surgical robot. Surgical robots have become increasingly popular. However, they remain a passive master–slave system with no safeguards for adverse events like vascular injury. In the past, this is because the robotic systems do not have sufficient means of (1) tracking deformable tissues during surgery and, crucially, (2) semantically labeling the tracked data; e.g., blood vessel to protect versus kidney tissue to resect. Time of flight cameras have been proposed for tracking tissue geometry through an endoscope [4]. We herein propose to use time of flight to track and semantically label tissues in real time. Specifically, we evaluated the feasibility of time of flight tracking for semantically distinguishing kidney tissue from connected vasculature in real time using a modified algorithm.