Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT.

Journal: BMC medical imaging
Published Date: May 26, 2022

Abstract

OBJECTIVE: We aim to propose a deep learning-based method of automated segmentation of eight brain anatomical regions in head computed tomography (CT) images obtained during positron emission tomography/computed tomography (PET/CT) scans. The brain regions include basal ganglia, cerebellum, hemisphere, and hippocampus, all split into left and right.

Authors

  • Tong Wang
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China; Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan 030000, China.
  • Haiqun Xing
    Department of Nuclear Medicine, Peking Union Medical College Hospital, Beijing, China.
  • Yige Li
    GE Healthcare China, Shanghai, China.
  • Sicong Wang
    Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
  • Ling Liu
    College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China. Electronic address: lliu9308@sina.com.
  • Fang Li
    Department of General Surgery, Chongqing General Hospital, Chongqing, China.
  • Hongli Jing
    Department of Nuclear Medicine, Peking Union Medical College Hospital, Beijing, China. annsmile1976@sina.com.

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