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Shear wave trajectory detection in ultra-fast M-mode images for liver fibrosis assessment: A deep learning-based line detection approach.

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Title: Shear wave trajectory detection in ultra-fast M-mode images for liver fibrosis assessment: A deep learning-based line detection approach.
Authors: Wang X; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region., Liu B; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region., Wu C; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region., Huang Z; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region., Zhou Y; School of Biomedical Engineering, University of Shenzhen, Shenzhen, China., Wu X; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region., Zheng Y; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address: ypzheng@ieee.org.
Source: Ultrasonics [Ultrasonics] 2024 Aug; Vol. 142, pp. 107358. Date of Electronic Publication: 2024 Jun 10.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 0050452 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1874-9968 (Electronic) Linking ISSN: 0041624X NLM ISO Abbreviation: Ultrasonics Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Wang+X%22">Wang X</searchLink>; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.<br /><searchLink fieldCode="AU" term="%22Liu+B%22">Liu B</searchLink>; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.<br /><searchLink fieldCode="AU" term="%22Wu+C%22">Wu C</searchLink>; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.<br /><searchLink fieldCode="AU" term="%22Huang+Z%22">Huang Z</searchLink>; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.<br /><searchLink fieldCode="AU" term="%22Zhou+Y%22">Zhou Y</searchLink>; School of Biomedical Engineering, University of Shenzhen, Shenzhen, China.<br /><searchLink fieldCode="AU" term="%22Wu+X%22">Wu X</searchLink>; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.<br /><searchLink fieldCode="AU" term="%22Zheng+Y%22">Zheng Y</searchLink>; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address: ypzheng@ieee.org.
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