TY - GEN
T1 - A Hybrid Deep Learning Pipeline for Improved Ultrasound Localization Microscopy
AU - Stevens, Tristan S.W.
AU - Herbst, Elizabeth B.
AU - Luijten, Ben
AU - Ossenkoppele, Boudewine W.
AU - Voskuil, Thierry J.
AU - Wang, Shiying
AU - Youn, Jihwan
AU - Errico, Claudia
AU - Pezzotti, Nicola
AU - More Authors, null
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85143769265&partnerID=8YFLogxK
U2 - 10.1109/IUS54386.2022.9958562
DO - 10.1109/IUS54386.2022.9958562
M3 - Conference contribution
AN - SCOPUS:85143769265
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2022 - IEEE International Ultrasonics Symposium
PB - IEEE
T2 - 2022 IEEE International Ultrasonics Symposium, IUS 2022
Y2 - 10 October 2022 through 13 October 2022
ER -