A Non-Linear Image Registration Scheme for Real-Time Liver Ultrasound Tracking using Normalized Gradient Fields

We propose a novel scheme for annotation tracking in long liver ultrasound sequences. It is based on a variational non-linear image registration method using Normalized Gradient Fields, extended by a moving window strategy based on registrations to the provided anno-tation on the first frame. By this we achieve robustness against error accumulation, while handling large deformations at the same time. The method is evaluated on 21 datasets with up to five annotations as contri-bution to the MICCAI CLUST14 challenge. We achieved a mean tracking error of 1.31 mm with a standard deviation of 1.63 mm, while running at close to real-time speed, exceeding acquisition rate in ten cases with up to 44 frames per second on standard hardware.