A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

Journal: Neuroradiology
PMID: 40183967

Abstract

PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was developed using preoperative MRI to assist clinicians in diagnosing pituitary microadenomas.

Authors

  • ChenGang Yuan
    Department of Neurosurgery, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225000, Jiangsu Province, China.
  • Hang Qu
  • Huming Dai
    College of Computer Science, Sichuan University, South Section 1, Yihuan Road, Chengdu, 610065, Sichuan, China.
  • HaiXiao Jiang
    Department of Neurosurgery, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225000, Jiangsu Province, China.
  • DeMao Cao
    Department of Neurosurgery, Affiliated Hospital of Yangzhou University, Yangzhou, 225000, Jiangsu Province, China.
  • LiYing Shao
    Department of Neurosurgery, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225000, Jiangsu Province, China.
  • Liangxue Zhou
    Department of Neurosurgery, West China Hospital, Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
  • AiJun Peng
    Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China. Electronic address: 13801456336@163.com.

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