A Hybrid Deep Learning Pipeline for Improved Ultrasound Localization Microscopy

A Hybrid Deep Learning Pipeline for Improved Ultrasound Localization Microscopy

Stevens, T.N.M. (Eindhoven University of Technology)
Herbst, Elizabeth B. (Philips Research)
Luijten, Ben (Eindhoven University of Technology)
Ossenkoppele, B.W. (TU Delft ImPhys/Imaging Physics; TU Delft ImPhys/Medical Imaging; Eindhoven University of Technology)
Voskuil, Thierry J. (Eindhoven University of Technology)
Wang, Shiying (Philips Research)
Youn, Jihwan (Eindhoven University of Technology)
Errico, Claudia (Philips Research)
Pezzotti, Nicola (Eindhoven University of Technology; Philips Research)

ImPhys/Imaging Physics

2022

The image quality of ultrasound localization microscopy (ULM) images is driven by the ability to accurately detect and track the location of microbubbles (MBs) in vascular networks. This task becomes increasingly challenging in imaging environments with high MB concentrations and low signal-to-noise ratios, making it difficult to differentiate and localize individual MBs. Recent developments in deep learning (DL) have demonstrated significant improvements over conventional methods but depend on vast amounts of realistic training data with the corresponding ground truth labels, which are difficult to obtain. The alternative, simulated data, in turn, poses challenges in generalizability of the method. In this work, we present a hybrid pipeline for ULM that comprises data generation, localization, and tracking. It combines the current state-of-the-art, utilizing both conventional and DL techniques. We show that using this approach, we can create high-quality velocity maps while being able to generalize well across different domains.

http://resolver.tudelft.nl/uuid:896a60a9-8b6a-4079-8dc7-6f80a69ae1d0

https://doi.org/10.1109/IUS54386.2022.9958562

IEEE

2023-07-01

9781665466578

IUS 2022 - IEEE International Ultrasonics Symposium

2022 IEEE International Ultrasonics Symposium, IUS 2022, 2022-10-10 → 2022-10-13, Venice, Italy

IEEE International Ultrasonics Symposium, IUS, 1948-5719, 2022-October

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 T.N.M. Stevens, Elizabeth B. Herbst, Ben Luijten, B.W. Ossenkoppele, Thierry J. Voskuil, Shiying Wang, Jihwan Youn, Claudia Errico, Nicola Pezzotti, More Authors