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

Daniel Wulff, Jannis Hagenah, Floris Ernst

Keywords: radiotherapy, deep learning, latent space, clustering

Abstract: 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 with which recognizing anatomical structures in different time frames is capable would be useful. In this study, 3DUS patches are reduced into a 128-dimensional representation space using conventional autoencoder, variational autoencoder and sliced-wasserstein autoencoder. In the representation 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 representation 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 representation space. The results indicate that the investigated autoencoders have different levels of usability for target tracking in 3DUS.

Paper Type: both

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Other

Paper Status: original work, not submitted yet

Source Code Url: The source code can be made available by contacting the corresponding author.

Data Set Url: The dataset used in this paper was first published in: Ipsen et al., "Towards automated ultrasound imaging - robotic image acquisition in liver and prostate for long-term motion monitoring", Physics in Medicine & Biology, 2021. The dataset is available by contacting the corresponding author of that paper.

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