Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

Journal: Neuroradiology
Published Date: Jan 26, 2021

Abstract

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types.

Authors

  • Antonio Di Ieva
    Neurosurgery Unit, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
  • Carlo Russo
    Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
  • Sidong Liu
    Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australia; Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
  • Anne Jian
    Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.
  • Michael Y Bai
    Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.
  • Yi Qian
    Jinhua People's Hospital, Jinhua, China. qianyicosta@163.com.
  • John S Magnussen
    Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.

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