Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound
This work addresses tracking in radiation therapy using 3D ultrasound without fiducials, but it is incremental as it compares existing autoencoder techniques on a specific dataset.
The study compared autoencoder variants for learning representations from 3D ultrasound patches to distinguish anatomical structures and cluster similar patches, showing that these methods have varying usability for target tracking in radiation therapy.
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.