Acute and sub-acute stroke lesion segmentation from multimodal MRI.

Journal: Computer methods and programs in biomedicine
Published Date: May 6, 2020

Abstract

BACKGROUND AND OBJECTIVE: Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment.

Authors

  • Albert Clèrigues
    Institute of Computer Vision and Robotics, University of Girona, Spain. Electronic address: albert.clerigues@udg.edu.
  • Sergi Valverde
    Research institute of Computer Vision and Robotics, University of Girona, Spain. Electronic address: svalverde@eia.udg.edu.
  • Jose Bernal
    Institute of Computer Vision and Robotics, University of Girona, Ed. P-IV, Campus Montilivi, Girona, 17003, Spain. Electronic address: jose.bernal@udg.edu.
  • Jordi Freixenet
    Institute of Computer Vision and Robotics, University of Girona, Spain.
  • Arnau Oliver
    Research institute of Computer Vision and Robotics, University of Girona, Spain.
  • Xavier Lladó
    Research institute of Computer Vision and Robotics, University of Girona, Spain.

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