Estimation of Incision Patterns Based on Visual Tracking of Surgical Tools in Minimally Invasive Surgery

In minimally invasive surgery, the positions of surgical tools are important in multiple instruments set-up and procedures. Typically, each surgery requires 4–5 incision holes and for each specific procedure, the layout of points defines specific pattern. Taking advantage of this possible one-to-one relationship between a specific procedure in minimally invasive surgery and the incision patterns, such patterns can be utilized in tele-monitoring of trainee during an emulated surgical operation. For example, in performance evaluation of trainee, this procedure would automatically estimate and verify the initial incision pattern to that of the predefined expected template associated with a particular surgical procedure. In this paper, we propose and analyze two models, based on color and shape respectively, to reconstruct the pattern. Both approaches use image information only to reconstruct the incision patterns in three dimensional space. The challenge of monocular endoscopic view is the lack of depth perception which hindered the vision-based tracking of laparoscopic tools. To address the problem, we present a method to determine not only the spatial tip position of the surgical tools, but also their orientation with respect to the camera coordinate frame. Detailed formulation shows that how segmented tool edges and camera field of view localize the 3D orientations of tools. Then, 3D position of the tool tip is reconstructed using either color or edge detection method. Finally, the orientations and the position of tool tips uniquely determine the poses of the tools. From above procedures, geometrical models of cylindrical tools can be constructed in each sequence of mono-camera images. To further use the tracking result in order to localize the incision point, we computed the vectors of the cylindrical tool center lines at multiple poses at number of frames. Extracted incision point is further analyzed as a recognition pattern to map into the patients’ pre-operative incision procedure. Accuracy of 3D tool pose estimation and incision pattern is evaluated in real image sequences with known ground truth.