Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Jun 9, 2020

Abstract

BACKGROUND: Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable.

Authors

  • Lan Wang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Jingwen Tan
    Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
  • Yingqian Ge
    Siemens Ltd. China, Shanghai, PR China.
  • Xinwei Tao
    Siemens Ltd. China, Shanghai, PR China.
  • Zheng Cui
    Siemens Shanghai Medical Equipment Ltd., Shanghai, PR China.
  • Zhenyu Fei
    Siemens Shanghai Medical Equipment Ltd., Shanghai, PR China.
  • Jing Lu
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Huan Zhang
    Department of Plant Protection, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
  • Zilai Pan
    Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.

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