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Yuan Gao - IEEE Xplore Author Profile
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Yuan Gao

Publications
2
Citations
50
Publications by Year
20222022
Co-Authors:
Show All Co-Authors (6)
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Yuan Gao

Affiliation

Chair for Computer Aided Medical Procedures and Augmented Reality (CAMP)
Technical University of Munich
Garching, Germany
Institute of Forming Technology and Equipment and Institute of Medical Robotics
Shanghai Jiao Tong University
Shanghai, China

Publication Topics

Publications
2
Citations
50
Publications by Year
20222022
Co-Authors:
Show All Co-Authors (6)
Author's Published Works

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Towards Autonomous Atlas-Based Ultrasound Acquisitions in Presence of Articulated Motion

Zhongliang Jiang;Yuan Gao;Le Xie;Nassir Navab

IEEE Robotics and Automation Letters
Year: 2022 | Volume: 7, Issue: 3 | Journal Article |
Cited by: Papers (23)
Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e.g. difficulty in guaranteeing intra- and inter-operator repeatability. However, due to anatomical and physiological variations between patients and relative movement of anatomical substructures, it is challenging to robustly generate optimal trajectories to examine the anatomies of interest, ...Show More

Towards Autonomous Atlas-Based Ultrasound Acquisitions in Presence of Articulated Motion

Zhongliang Jiang;Yuan Gao;Le Xie;Nassir Navab

Year: 2022 | Volume: 7, Issue: 3 | Journal Article |

VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation

Yuan Bi;Zhongliang Jiang;Yuan Gao;Thomas Wendler;Angelos Karlas;Nassir Navab

IEEE Robotics and Automation Letters
Year: 2022 | Volume: 7, Issue: 3 | Journal Article |
Cited by: Papers (27)
Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement lea...Show More

VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation

Yuan Bi;Zhongliang Jiang;Yuan Gao;Thomas Wendler;Angelos Karlas;Nassir Navab

Year: 2022 | Volume: 7, Issue: 3 | Journal Article |

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