@inproceedings{a6adc2318ede4b42981c225186f04207,
title = "Modified Residual Dense Network based super-resolution localization method for high-concentration microbubbles",
abstract = "The practical limitation of ultrasound localization microscopy for clinical translation is the trade-off between microbubble concentration and data acquisition time. Recently, deep learning-based approaches have shown promising capability in microbubble localization accuracy when using a high-concentration microbubble injection to shorten acquisition time. In this study, we construct a Modified Residual Dense Network (MRDN) for high-concentration microbubble super-resolution localization. By subjecting the collected data to non-local mean filtering operations, the MRDN is used for continuous learning. This method can be well used at high concentrations of 16 mathbf\{mm\}\{ boldsymbol\{-2\}\} with a high localization accuracy (localization error boldsymbol\{:22.3 mu\} mathbf\{m\}) and high localization reliability (Jaccard index:0.78).",
keywords = "Deep Learning, high-concentration, microbubble localization",
author = "Shizhe An and Mei Qu and Anqi Huang and Haiyang Yu and Yuebo Wang and Minxi Wan and Yujin Zong",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Ultrasonics Symposium, IUS 2022 ; Conference date: 10-10-2022 Through 13-10-2022",
year = "2022",
doi = "10.1109/IUS54386.2022.9958063",
language = "英语",
series = "IEEE International Ultrasonics Symposium, IUS",
publisher = "IEEE Computer Society",
booktitle = "IUS 2022 - IEEE International Ultrasonics Symposium",
}