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)


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.


  1. 1.
    Riedel, C.H., Zimmermann, P., Jensen-Kondering, U., Stingele, R., Deuschl, G., Jansen, O.: The importance of size successful recanalization by intravenous thrombolysis in acute anterior stroke depends on thrombus length. Stroke 42(6), 1775–1777 (2011)CrossRefGoogle Scholar
  2. 2.
    Huang, X., Cheripelli, B.K., Lloyd, S.M., Kalladka, D., Moreton, F.C., Siddiqui, A., Ford, I., Muir, K.W.: Alteplase versus tenecteplase for thrombolysis after ischaemic stroke (ATTEST): a phase 2, randomised, open-label, blinded endpoint study. Lancet Neurol. 14(4), 368–376 (2015)CrossRefGoogle Scholar
  3. 3.
    MacDougall, N.J., McVerry, F., Huang, X., Welch, A., Fulton, R., Muir, K.W.: Post-stroke hyperglycaemia is associated with adverse evolution of acute ischaemic injury. In: Cerebrovascular Diseases, vol. 37, p. 267, Karger Allschwilerstrasse, Basel (2014)Google Scholar
  4. 4.
    Wardlaw, J.M., Muir, K.W., Macleod, M.J., Weir, C., McVerry, F., Carpenter, T., Shuler, K., Thomas, R., Acheampong, P., Dani, K., Murray, A.: Clinical relevance and practical implications of trials of perfusion and angiographic imaging in patients with acute ischaemic stroke: a multicentre cohort imaging study. J. Neurol. Neurosurg. Psychiatry 84(9), 1001–1007 (2013)CrossRefGoogle Scholar
  5. 5.
    Dabbah, M.A., et al.: Detection and location of 127 anatomical landmarks in diverse ct datasets. In: SPIE Medical Imaging, p. 903415. International Society for Optics and Photonics (2014)Google Scholar
  6. 6.
    Lisowska, A., Bereridge, E., Muir, K., Poole, I.: Thrombus detection in CT brain scans using a convolutional neural network. In: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), Bioimaging, vol. 2, pp. 24–33. Scitepress (2017)Google Scholar
  7. 7.
    Lisowska, A., O‘Neil, A., Dilys, V., Daykin, M., Beveridge, E., Muir, K., Mclaughlin, S., Poole, I.: Context-aware convolutional neural networks for stroke sign detection in non-contrast CT scans. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 494–505. Springer, Cham (2017). Scholar
  8. 8.
    Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 589–596. Springer, Cham (2015). Scholar
  9. 9.
    Chollet, F.: Keras (2015).
  10. 10.
    Theano Development Team: Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688, May 2016
  11. 11.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  12. 12.
    Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. arXiv preprint arXiv:1708.02002 (2017)
  13. 13.
    Mair, G., Boyd, E.V., Chappell, F.M., von Kummer, R., Lindley, R.I., Sandercock, P., Wardlaw, J.M.: Sensitivity and specificity of the hyperdense artery sign for arterial obstruction in acute ischemic stroke. Stroke 46(1), 102–107 (2015)CrossRefGoogle Scholar
  14. 14.
    Campbell, B.C., Donnan, G.A., Mitchell, P.J., Davis, S.M.: Endovascular thrombectomy for stroke: current best practice and future goals. Stroke Vascu. Neurol. 1(1), 16–22 (2016)CrossRefGoogle Scholar

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

Personalised recommendations