  Image-guided radiation therapy can benefit from accurate motion tracking by
ultrasound imaging, in order to minimize treatment margins and radiate moving
anatomical targets, e.g., due to breathing. One way to formulate this tracking
problem is the automatic localization of given tracked anatomical landmarks
throughout a temporal ultrasound sequence. For this, we herein propose a
fully-convolutional Siamese network that learns the similarity between pairs of
image regions containing the same landmark. Accordingly, it learns to localize
and thus track arbitrary image features, not only predefined anatomical
structures. We employ a temporal consistency model as a location prior, which
we combine with the network-predicted location probability map to track a
target iteratively in ultrasound sequences. We applied this method on the
dataset of the Challenge on Liver Ultrasound Tracking (CLUST) with competitive
results, where our work is the first to effectively apply CNNs on this tracking
problem, thanks to our temporal regularization.
