J
Jie Yuan
Researcher at Nanjing University
Publications - 72
Citations - 951
Jie Yuan is an academic researcher from Nanjing University. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 12, co-authored 69 publications receiving 667 citations. Previous affiliations of Jie Yuan include Tongji University & University of Michigan.
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Medical breast ultrasound image segmentation by machine learning.
TL;DR: This paper proposes to use convolutional neural networks (CNNs) for segmenting breast ultrasound images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three‐dimensional (3D) Breast ultrasound images.
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Light Emitting Diodes based Photoacoustic Imaging and Potential Clinical Applications.
Yunhao Zhu,Guan Xu,Jie Yuan,Janggun Jo,Girish Gandikota,Hakan Demirci,Toshitaka Agano,Naoto Sato,Yusuke Shigeta,Xueding Wang +9 more
TL;DR: The development of LED-based PA imaging integrated with B-mode ultrasound, which could be a promising tool for several clinical applications, such as assessment of peripheral microvascular function and dynamic changes, diagnosis of inflammatory arthritis, and detection of head and neck cancer.
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The Functional Pitch of an Organ: Quantification of Tissue Texture with Photoacoustic Spectrum Analysis
TL;DR: The results supported the hypothesis that the PASA allows quantitative identification of the microstructural changes that differentiate normal from fatty livers and compared with that at 532 nm, P ASA at 1200 nm is more reliable for fatty liver diagnosis.
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Real-time photoacoustic and ultrasound dual-modality imaging system facilitated with graphics processing unit and code parallel optimization
TL;DR: A fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction, and display for both photoacoustic (PA) and US imaging all in a truly real-time manner, is reported.
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Automated 3D ultrasound image segmentation to aid breast cancer image interpretation
TL;DR: An automated algorithm to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer is proposed.