A fully autonomous robotic ultrasound system for thyroid scanning.

Journal: Nature communications
Published Date:

Abstract

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

Authors

  • Kang Su
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China.
  • Jingwei Liu
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China.
  • Xiaoqi Ren
    School of Future Technology, South China University of Technology, Guangzhou, 511442, China.
  • Yingxiang Huo
    School of Future Technology, South China University of Technology, Guangzhou, 511442, China.
  • Guanglong Du
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Xueqian Wang
    Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China. wang.xq@sz.tsinghua.edu.cn.
  • Bin Liang
    Image Processing Center, Beihang University, Beijing 100191, People's Republic of China. Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China.
  • Di Li
    Department of Urology, General Hospital of the Air Force, PLA, No. 30 Fucheng Road Haidian District, Beijing, 100142 China.
  • Peter Xiaoping Liu