Ultrasound Microbubbles Localization Using Object Detection Model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ultrasound Localization Microscopy (ULM) is a newly developing imaging technique capable of achieving sub-wave diffraction resolution for micro-vascular structures. However, traditional single microbubble (MB) localization methods have problems with precision, robustness, and computational efficiency. In this work, we propose a convolutional neural network (CNN) to localize MBs by converting the localization problem to an object detection problem. To identify the location of MBs in an image, our proposed network progressively extracted the features and output the coordinates of the detected MBs. This work compared the proposed method and Gaussian fit model on ultrasound images with realistic flow characteristics, generated by the IUS ULTRASR Challenge, using Jaccard index and localization precision as metrics. The proposed method outperformed the Gaussian fit model in terms of Jaccard index, precision, and processing time.

Original languageEnglish (US)
Title of host publicationIUS 2023 - IEEE International Ultrasonics Symposium, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350346459
DOIs
StatePublished - 2023
Event2023 IEEE International Ultrasonics Symposium, IUS 2023 - Montreal, Canada
Duration: Sep 3 2023Sep 8 2023

Publication series

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

Conference

Conference2023 IEEE International Ultrasonics Symposium, IUS 2023
Country/TerritoryCanada
CityMontreal
Period9/3/239/8/23

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

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