Investigation of the Hashing Algorithm Extension of Depth Image Matching for Liver Surgery

We have developed some liver posture estimation methods for achieving a liver surgical navigation system that can support surgeons who needs to know very precise information about vessels in the liver. Those methods use 3D liver models scanned from patients and 2D images scanned by depth cameras for estimating the liver posture as accurately as possible. Since a new posture estimation method using simple and high-speed image hashing algorithm was developed last year, we are trying to improve the method in accuracy and applicability for the real-time liver posture tracking. In this paper, we examine how deep learning methods can be used for liver posture estimation and its tracking over the 2D images scanned from depth cameras. We study how can a multi-layer perceptron neural network learn and estimate the liver rotation expressed in quaternion form. The real-time surgical navigation system should be efficiently implemented by combining multiple estimation methods including the deep learning method.