  Accurate tracking of an anatomical landmark over time has been of high
interests for disease assessment such as minimally invasive surgery and tumor
radiation therapy. Ultrasound imaging is a promising modality benefiting from
low-cost and real-time acquisition. However, generating a precise landmark
tracklet is very challenging, as attempts can be easily distorted by different
interference such as landmark deformation, visual ambiguity and partial
observation. In this paper, we propose a long-short diffeomorphic motion
network, which is a multi-task framework with a learnable deformation prior to
search for the plausible deformation of landmark. Specifically, we design a
novel diffeomorphism representation in both long and short temporal domains for
delineating motion margins and reducing long-term cumulative tracking errors.
To further mitigate local anatomical ambiguity, we propose an expectation
maximisation motion alignment module to iteratively optimize both long and
short deformation, aligning to the same directional and spatial representation.
The proposed multi-task system can be trained in a weakly-supervised manner,
which only requires few landmark annotations for tracking and zero annotation
for long-short deformation learning. We conduct extensive experiments on two
ultrasound landmark tracking datasets. Experimental results show that our
proposed method can achieve better or competitive landmark tracking performance
compared with other state-of-the-art tracking methods, with a strong
generalization capability across different scanner types and different
ultrasound modalities.
