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

• URL: http://arxiv.org/abs/2201.03319v1
• Date: Mon, 10 Jan 2022 12:38:22 GMT
• Title: Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound
• Authors: Daniel Wulff, Jannis Hagenah, Floris Ernst
• Abstract summary: 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. 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.
• Score: 0.7734726150561088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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