Current trends in glioma tumor segmentation: A survey of deep learning modules.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jun 2, 2025

Abstract

BACKGROUND: Multiparametric Magnetic Resonance Imaging (mpMRI) is the gold standard for diagnosing brain tumors, especially gliomas, which are difficult to segment due to their heterogeneity and varied sub-regions. While manual segmentation is time-consuming and error-prone, Deep Learning (DL) automates the process with greater accuracy and speed.

Authors

  • Fereshteh Khodadadi Shoushtari
    Quantitative MR Imaging and Spectroscopy Group, Advanced Medical Technologies and Equipment Institute, Tehran University of Medical Sciences, Tehran, Iran; Nuclear Engineering Department, Shiraz University, Shiraz, Iran.
  • Reza Elahi
    Department of Radiology, Zanjan University of Medical Sciences, Zanjan, Iran. rezaelahi96@zums.ac.ir.
  • Gelareh Valizadeh
    Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran.
  • Farzan Moodi
    Eye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Sattarkhan-Niaiesh St., Tehran, 11335, Iran.
  • Hanieh Mobarak Salari
    Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran.
  • Hamidreza Saligheh Rad
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China.

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