Navigating inner space: 3-D assistance for minimally invasive surgery

Abstract

Abstract-Since its inception about three decades ago, modern minimally invasive surgery has made huge advances in both technique and technology. However, the minimally invasive surgeon is still faced with daunting challenges in terms of visualization and hand-eye coordination.At the Center for Computer Integrated Surgical Systems and Technology (CISST) we have been developing a set of techniques for assisting surgeons in navigating and manipulating the three-dimensional space within the human body. In order to develop such systems, a variety of challenging visual tracking, reconstruction and registration problems must be solved. In addition, this information must be tied to methods for assistance that improve surgical accuracy and reliability but allow the surgeon to retain ultimate control of the procedure and do not prolong time in the operating room.In this article, we present two problem areas, eye microsurgery and thoracic minimally invasive surgery, where computational vision can play a role. We then describe methods we have developed to process video images for relevant geometric information, and related control algorithms for providing interactive assistance. Finally, we present results from implemented systems.

Content-based processing and analysis of endoscopic images and videos: A survey

“…A virtual fixture [140,177] is a constraint that reduces the precision requirements. It can be used to define forbidden regions or a safety margin around critical anatomical structures, which must not be damaged, in order to prevent erroneous moves, or to simplify the execution of a task by "guiding" the instrument motion along a safe corridor [30]. The long-term vision is to enable commonly occurring tasks like suturing to be executed autonomously by high-level command of the surgeon (e.g., by pointing at the target position with a laser pointer) [105,175,220].…”

Challenges in image-guided therapy system design

“…More recently, tests have been made under actual OR conditions in animal or human models. For example, the Hager group at Johns Hopkins University has analyzed the kinematic data in the DaVinci system (Burschka, Corso et al, 2005), and the Vosburgh group at CIMIT/BWH has studied the performance kinematics and also the display utility in laparoscopic and endoscopic systems (Vosburgh, Stylopoulos et al, 2007). At this level, various possible system error modes can be delineated and avoidance, mitigation, or response plans developed.…”

Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

“…Among the many approaches proposed in the last twenty years [1,2,6,7,8,9], recent detection-based schemes that rely on building statistical classifiers to evaluate the presence of the instrument appear to be the most promising for in-vivo detection and tracking [4,5,10]. Within this last category of methods, Reiter et al [5] combined a multiclass Random Forest (RF) [11] labelling approach with robot kinematic information to estimate the instrument 3D pose.…”