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Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans

  • Felix DenzingerEmail author
  • Michael Wels
  • Katharina Breininger
  • Anika Reidelshöfer
  • Joachim Eckert
  • Michael Sühling
  • Axel Schmermund
  • Andreas Maier
Conference paper
  • 48 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this work we compare and improve three deep learning algorithms for this task: A 3D recurrent convolutional neural network (RCNN), a 2D multi-view ensemble approach based on texture analysis, and a newly proposed 2.5D approach. Current state of the art methods utilising fluid dynamics based fractional flow reserve (FFR) simulation reach an AUC of up to 0.93 for the task of predicting an abnormal invasive FFR value. For the comparable task of predicting revascularisation decision, we are able to improve the performance in terms of AUC of both existing approaches with the proposed modifications, specifically from 0.80 to 0.90 for the 3D-RCNN, and from 0.85 to 0.90 for the multi-view texture-based ensemble. The newly proposed 2.5D approach achieves comparable results with an AUC of 0.90.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Felix Denzinger
    • 1
    • 2
    Email author
  • Michael Wels
    • 2
  • Katharina Breininger
    • 1
  • Anika Reidelshöfer
    • 3
  • Joachim Eckert
    • 4
  • Michael Sühling
    • 2
  • Axel Schmermund
    • 4
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-University Erlangen-NurembergErlangenDeutschland
  2. 2.Computed TomographySiemens HealthineersForchheimDeutschland
  3. 3.University Clinic FrankfurtFrankfurt am MainDeutschland
  4. 4.Cardioangiological Centrum BethanienFrankfurt am MainDeutschland

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