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.
@article{arxiv.2201.03319,
title = {Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound},
author = {Daniel Wulff and Jannis Hagenah and Floris Ernst},
journal= {arXiv preprint arXiv:2201.03319},
year = {2022}
}
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Presented at Medical Imaging with Deep Learning (MIDL) 2021