Tissue Recognition with Deep Ensemble Learning of Ultrasound Wavelet Spectra

Zhun Xie; Nan Ji; Lijun Xu; Jianguo Ma

doi:10.1109/IUS52206.2021.9593661

Tissue Recognition with Deep Ensemble Learning of Ultrasound Wavelet Spectra

Zhun Xie
, Nan Ji
, Lijun Xu
, Jianguo Ma

仪器科学与光电工程学院

Beihang University
Capital Medical University

科研成果: 期刊稿件 › 会议文章 › 同行评审

2 引用（Scopus）

摘要

Conventional ultrasound imaging presents the scattering amplitude of echo signal by local brightness whereas neglecting the spectral information. However, ultrasound harmonic imaging demonstrated that ultrasound echoes carry abundant spectral information that varies among organs, tissues, and microbubble contrasts. We hypothesize that the spectral pattern potentially acts as a fingerprint to identify the tissues in various organs, which may lead to high contrast ultrasonic imaging and easier interpretation.

源语言	英语
期刊	IEEE International Ultrasonics Symposium, IUS
DOI	https://doi.org/10.1109/IUS52206.2021.9593661
出版状态	已出版 - 2021
活动	2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, 中国期限: 11 9月 2011 → 16 9月 2011

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10.1109/IUS52206.2021.9593661

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title = "Tissue Recognition with Deep Ensemble Learning of Ultrasound Wavelet Spectra",

abstract = "Conventional ultrasound imaging presents the scattering amplitude of echo signal by local brightness whereas neglecting the spectral information. However, ultrasound harmonic imaging demonstrated that ultrasound echoes carry abundant spectral information that varies among organs, tissues, and microbubble contrasts. We hypothesize that the spectral pattern potentially acts as a fingerprint to identify the tissues in various organs, which may lead to high contrast ultrasonic imaging and easier interpretation.",

keywords = "RF signals, deep ensemble learning, tissue recognition, wavelet spectra",

author = "Zhun Xie and Nan Ji and Lijun Xu and Jianguo Ma",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Ultrasonics Symposium, IUS 2021 ; Conference date: 11-09-2011 Through 16-09-2011",

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doi = "10.1109/IUS52206.2021.9593661",

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journal = "IEEE International Ultrasonics Symposium, IUS",

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T1 - Tissue Recognition with Deep Ensemble Learning of Ultrasound Wavelet Spectra

AU - Xie, Zhun

AU - Ji, Nan

AU - Xu, Lijun

AU - Ma, Jianguo

PY - 2021

Y1 - 2021

N2 - Conventional ultrasound imaging presents the scattering amplitude of echo signal by local brightness whereas neglecting the spectral information. However, ultrasound harmonic imaging demonstrated that ultrasound echoes carry abundant spectral information that varies among organs, tissues, and microbubble contrasts. We hypothesize that the spectral pattern potentially acts as a fingerprint to identify the tissues in various organs, which may lead to high contrast ultrasonic imaging and easier interpretation.

AB - Conventional ultrasound imaging presents the scattering amplitude of echo signal by local brightness whereas neglecting the spectral information. However, ultrasound harmonic imaging demonstrated that ultrasound echoes carry abundant spectral information that varies among organs, tissues, and microbubble contrasts. We hypothesize that the spectral pattern potentially acts as a fingerprint to identify the tissues in various organs, which may lead to high contrast ultrasonic imaging and easier interpretation.

KW - RF signals

KW - deep ensemble learning

KW - tissue recognition

KW - wavelet spectra

UR - https://www.scopus.com/pages/publications/85122892517

U2 - 10.1109/IUS52206.2021.9593661

DO - 10.1109/IUS52206.2021.9593661

M3 - 会议文章

AN - SCOPUS:85122892517

SN - 1948-5719

JO - IEEE International Ultrasonics Symposium, IUS

JF - IEEE International Ultrasonics Symposium, IUS

T2 - 2021 IEEE International Ultrasonics Symposium, IUS 2021

Y2 - 11 September 2011 through 16 September 2011

ER -

Tissue Recognition with Deep Ensemble Learning of Ultrasound Wavelet Spectra

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