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)


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.


Graph convolutional networks Coronary CT angiography Coronary arteries Lumen segmentation 



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


  1. 1.
    Cucurull, G., et al.: Convolutional neural networks for mesh-based parcellation of the cerebral cortex. In: Medical Imaging with Deep Learning (MIDL) (2018)Google Scholar
  2. 2.
    Freiman, M., et al.: Improving CCTA-based lesions’ hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation. Med. Phys. 44(3), 1040–1049 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems (NIPS), pp. 1024–1034 (2017)Google Scholar
  4. 4.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)Google Scholar
  5. 5.
    Kirişli, H., et al.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 17(8), 859–876 (2013)CrossRefGoogle Scholar
  6. 6.
    Lee, M.C.H., Petersen, K., Pawlowski, N., Glocker, B., Schaap, M.: Tetris: template transformer networks for image segmentation with shape priors. IEEE Trans. Med. Imag. (2019). CrossRefGoogle Scholar
  7. 7.
    Leipsic, J., et al.: SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the society of cardiovascular computed tomography guidelines committee. J. Cardiovasc. Comput. Tomogr. 8(5), 342–358 (2014)CrossRefGoogle Scholar
  8. 8.
    Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  9. 9.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  10. 10.
    Lugauer, F., Zheng, Y., Hornegger, J., Kelm, B.M.: Precise lumen segmentation in coronary computed tomography angiography. In: Menze, B., et al. (eds.) MCV 2014. LNCS, vol. 8848, pp. 137–147. Springer, Cham (2014). Scholar
  11. 11.
    Selvan, R., Kipf, T., Welling, M., Pedersen, J.H., Petersen, J., de Bruijne, M.: Extraction of airways using graph neural networks. In: Medical Imaging with Deep Learning (MIDL) (2018)Google Scholar
  12. 12.
    Taylor, C.A., Fonte, T.A., Min, J.K.: Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J. Am. Coll. Cardiol. 61(22), 2233–2241 (2013)CrossRefGoogle Scholar
  13. 13.
    Wolterink, J.M., van Hamersvelt, R.W., Viergever, M.A., Leiner, T., Išgum, I.: Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med. Image Anal. 51, 46–60 (2019)CrossRefGoogle Scholar

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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