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Fully Automatic Segmentation for Ischemic Stroke Using CT Perfusion Maps

  • Vikas Kumar Anand
  • Mahendra Khened
  • Varghese Alex
  • Ganapathy KrishnamurthiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

We propose an algorithm for automatic segmentation of ischemic lesion using CT perfusion maps. Our method is based on encoder-decoder fully convolutional neural network approach. The pre-processing step involves skull stripping and standardization of perfusion maps and extraction of slices with lesions as the training data. These CT perfusion maps are used to train the proposed network for automatic segmentation of stroke lesions. The network is trained by minimizing the weighted combination of cross entropy and dice losses. Our algorithm achieves 0.43, 0.53 and 0.45 Dice, precision, and recall respectively on challenge test data set.

Keywords

Deep learning CNN CT perfusion Ischemic stroke 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vikas Kumar Anand
    • 1
  • Mahendra Khened
    • 1
  • Varghese Alex
    • 1
  • Ganapathy Krishnamurthi
    • 1
    Email author
  1. 1.Indian Institute of Technology MadrasChennaiIndia

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