# Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound

arXiv: https://arxiv.org/abs/2201.03319 · HTML: https://mlsanity.com/paper/2201.03319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model](https://mlsanity.com/paper/1804.01947/) (2018)

## Abstract (arXiv)

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.