Minimally invasive surgery (MIS) poses visual challenges to the surgeons. In MIS, binocular disparity is not freely available for surgeons, who are required to mentally rebuild the 3-dimensional (3D) patient anatomy from a limited number of monoscopic visual cues. The insufficient depth cues from the MIS environment could cause surgeons to misjudge spatial depth, which could lead to performance errors thus jeopardizing patient safety. In this article, we will first discuss the natural human depth perception by exploring the main depth cues available for surgeons in open procedures. Subsequently, we will reveal what depth cues are lost in MIS and how surgeons compensate for the incomplete depth presentation. Next, we will further expand our knowledge by exploring some of the available solutions for improving depth presentation to surgeons. Here we will review the innovative approaches (multiple 2D camera assembly, shadow introduction) and devices (3D monitors, head-mounted devices, and auto-stereoscopic monitors) for 3D image presentation from the past few years.

By applying our method of calculating convergence points using eye tracking, we were able to elicit the eye movement patterns of human operators between the normal and stereovision conditions. Knowledge from this study can be applied to the design of surgical visual systems, with the goal of improving surgical performance and patient safety.

Abstract-Laparoscopic Surgery (LS) is a modern surgical technique whereby the surgery is performed through an incision with tools and camera as opposed to conventional open surgery. This promises minimal recovery times and less hemorrhaging. Multi view LS is the latest development in the field, where the system uses multiple cameras to give the surgeon more information about the surgical site, potentially making the surgery easier. In this publication, we study the gaze patterns of a high performing subject in a multi-view LS environment and compare it with that of a novice to detect the differences between the gaze behavior. This was done by conducting a user study with 20 university students with varying levels of expertise in Multi-view LS. The subjects performed an laparoscopic task in simulation with three cameras (front/top/side). The subjects were then separated as high and low performers depending on the performance times and their data was analyzed. Our results show statistically significant differences between the two behaviors. This opens up new areas from of training novices to Multi-view LS to making smart displays that guide your shows the optimum view depending on the situation.