  3D ultrasound (3DUS) becomes more interesting for target tracking in
radiation therapy due to its capability to provide volumetric images in
real-time without using ionizing radiation. It is potentially usable for
tracking without using fiducials. For this, a method for learning meaningful
representations would be useful to recognize anatomical structures in different
time frames in representation space (r-space). In this study, 3DUS patches are
reduced into a 128-dimensional r-space using conventional autoencoder,
variational autoencoder and sliced-wasserstein autoencoder. In the r-space, the
capability of separating different ultrasound patches as well as recognizing
similar patches is investigated and compared based on a dataset of liver
images. Two metrics to evaluate the tracking capability in the r-space are
proposed. It is shown that ultrasound patches with different anatomical
structures can be distinguished and sets of similar patches can be clustered in
r-space. The results indicate that the investigated autoencoders have different
levels of usability for target tracking in 3DUS.
