Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging.

Journal: Seminars in ultrasound, CT, and MR
Published Date: Feb 11, 2022

Abstract

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.

Authors

  • Guangming Zhu
    1 Department of Radiology, Neuroradiology Division, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Bin Jiang
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Fei Chen
    Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
  • Yuan Xie
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.

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