Automatic pituitary adenoma segmentation and identification of cavernous sinus invasion via multitask learning.

Journal: Clinical radiology
PMID:

Abstract

AIM: This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) segmentation and identification of cavernous sinus (CS) invasion.

Authors

  • W Rui
    Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, China. Electronic address: wennyrui@126.com.
  • W Gao
    Department of Neurosurgery, Peking University Third Hospital, Beijing, 100191, China. Electronic address: gaowenyuan@bjmu.edu.cn.
  • N Qiao
    Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, China; Neurosurgical Institute of Fudan University, Shanghai, China; Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China; Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China. Electronic address: norikaisa@qq.com.
  • X Chen
    Division of Infectious Diseases,The People's Hospital of Meizhou,Meizhou,China.
  • M Han
    Stanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California.
  • Y Wu
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, USA.
  • T Xin
    Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China; Medical Science and Technology Innovation Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China. Electronic address: drxintao@yeah.net.
  • J Yang
  • Y Zhao
    Department of Orthopedics, Yantai Shan Hospital, Yantai 264008, China.
  • Z Yao
    Department of Epidemiology and Health Statistics,Guangdong Pharmaceutical University,Guangzhou,China.