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Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

  • Jelmer M. WolterinkEmail author
  • Tim Leiner
  • Ivana Išgum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)

Abstract

Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Surface meshes enclosing the full coronary artery tree were automatically extracted. A quantitative evaluation on 78 coronary artery segments showed that these meshes corresponded closely to reference annotations, with a Dice similarity coefficient of 0.75/0.73, a mean surface distance of 0.25/0.28 mm, and a Hausdorff distance of 1.53/1.86 mm in healthy/diseased vessel segments. The results showed that inclusion of mesh information in a GCN improves segmentation overlap and accuracy over a baseline model without interaction on the mesh. The results indicate that GCNs allow efficient extraction of coronary artery surface meshes and that the use of GCNs leads to regular and more accurate meshes.

Keywords

Graph convolutional networks Coronary CT angiography Coronary arteries Lumen segmentation 

Notes

Acknowledgements

P15-26, Project 2, Dutch Technology Foundation with participation of Philips Healthcare.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jelmer M. Wolterink
    • 1
    • 2
    Email author
  • Tim Leiner
    • 3
  • Ivana Išgum
    • 1
    • 2
    • 4
  1. 1.Department of Biomedical Engineering and PhysicsAmsterdam University Medical CenterAmsterdamThe Netherlands
  2. 2.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  3. 3.Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
  4. 4.Department of Radiology and Nuclear MedicineAmsterdam University Medical CenterAmsterdamThe Netherlands

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