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

Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences