A Hybrid Deep Learning Pipeline for Improved Ultrasound Localization Microscopy

Tristan S.W. Stevens, Elizabeth B. Herbst, Ben Luijten, Boudewine W. Ossenkoppele, Thierry J. Voskuil, Shiying Wang, Jihwan Youn, Claudia Errico, Nicola Pezzotti, More Authors

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

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.

Original languageEnglish
Title of host publicationIUS 2022 - IEEE International Ultrasonics Symposium
PublisherIEEE
Number of pages4
ISBN (Electronic)9781665466578
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Ultrasonics Symposium, IUS 2022 - Venice, Italy
Duration: 10 Oct 202213 Oct 2022

Publication series

NameIEEE International Ultrasonics Symposium, IUS
Volume2022-October
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2022 IEEE International Ultrasonics Symposium, IUS 2022
Country/TerritoryItaly
CityVenice
Period10/10/2213/10/22

Bibliographical note

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.

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