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Evaluation of Dense Vessel Detection in NCCT Scans

  • Aneta Lisowska
  • Erin Beveridge
  • Alison O’NeilEmail author
  • Vismantas Dilys
  • Keith Muir
  • Ian Poole
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 881)

Abstract

Automatic detection and measurement of dense vessels may enhance the clinical workflow for treatment triage in acute ischemic stroke. In this paper we use a 3D Convolutional Neural Network, which incorporates anatomical atlas information and bilateral comparison, to detect dense vessels. We use 112 non-contrast computed tomography (NCCT) scans for training of the detector and 58 scans for evaluation of its performance. We compare automatic dense vessel detection to identification of the dense vessels by clinical researchers in NCCT and computed tomography angiography (CTA). The automatic system is able to detect dense vessel in NCCT scans, however it shows lower specificity in relation to CTA than clinical experts.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Aneta Lisowska
    • 1
    • 2
  • Erin Beveridge
    • 1
  • Alison O’Neil
    • 1
    Email author
  • Vismantas Dilys
    • 1
  • Keith Muir
    • 3
  • Ian Poole
    • 1
  1. 1.Toshiba Medical Visualization Systems Europe Ltd.EdinburghUK
  2. 2.School of Engineering and Physical SciencesHeriot-Watt University EdinburghUK
  3. 3.Queen Elizabeth University Hospital, University of GlasgowGlasgowUK

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