Performance and Accuracy Analysis of 3D Model Tracking for Liver Surgery

We are developing a liver surgical navigation system that uses a 3D liver model of the patient. As the liver contains several types of blood vessels, surgeons can sterically check the treating positions using the navigation system. For better navigation, the 3D liver model should be continuously and precisely matched with the patient's liver state, and the liver location and orientation should be calculated in real time. The location and orientation is estimated by comparing two depth images of the liver; one is captured from the patient's body using a depth camera, and one is rendered from the pre-scanned 3D liver model using OpenGL. The tracking is performed using gradient descent optimization algorithm. In this paper, we measure how the tracking agreement rate will change while the liver location and orientation are changing, so that we can understand the tracking performance, accuracy and the pitfall range of the navigation system.