TY - GEN
T1 - Ultrasound Microbubbles Localization Using Object Detection Model
AU - Liu, Xilun
AU - Almekkawy, Mohamed
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85178624297&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178624297&partnerID=8YFLogxK
U2 - 10.1109/IUS51837.2023.10307614
DO - 10.1109/IUS51837.2023.10307614
M3 - Conference contribution
AN - SCOPUS:85178624297
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2023 - IEEE International Ultrasonics Symposium, Proceedings
PB - IEEE Computer Society
T2 - 2023 IEEE International Ultrasonics Symposium, IUS 2023
Y2 - 3 September 2023 through 8 September 2023
ER -