Advertisement

Context-Aware Convolutional Neural Networks for Stroke Sign Detection in Non-contrast CT Scans

  • Aneta LisowskaEmail author
  • Alison O’Neil
  • Vismantas Dilys
  • Matthew Daykin
  • Erin Beveridge
  • Keith Muir
  • Stephen Mclaughlin
  • Ian Poole
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 723)

Abstract

Detection of acute stroke signs in non-contrast CT images is a challenging task. The intensity and texture variations in pathological regions are subtle and can be confounded by normal physiological changes or by old lesions. In this paper we investigate the use of contextual information for stroke sign detection. In particular, the appearance of the contralateral anatomy and the atlas-encoded spatial location are incorporated into a Convolutional Neural Network (CNN) architecture. CNNs are trained separately for the detection of dense vessels and of ischaemia. The network performance is evaluated on 170 datasets by cross-validation. We find that atlas location is important for dense vessel detection, but is less useful for ischaemia, whereas bilateral comparison is crucial for detection of ischaemia.

References

  1. 1.
    Wintermark, M., Albers, G.W., Broderick, J.P., Demchuk, A.M., Fiebach, J.B., Fiehler, J., Grotta, J.C., Houser, G., Jovin, T.G., Lees, K.R., et al.: Acute stroke imaging research roadmap II. Stroke 44(9), 2628–2639 (2013)CrossRefGoogle Scholar
  2. 2.
    Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput. Med. Imaging Graph. 31(4), 285–298 (2007)CrossRefGoogle Scholar
  3. 3.
    Dhawan, A.P., Loncaric, S., Hitt, K., Broderick, J., Brott, T.: Image analysis and 3-D visualization of intracerebral brain hemorrhage. In: Proceedings of Sixth Annual IEEE Symposium on Computer-Based Medical Systems, pp. 140–145. IEEE (1993)Google Scholar
  4. 4.
    Usinskas, A., Dobrovolskis, R.A., Tomandl, B.F.: Ischemic stroke segmentation on CT images using joint features. Informatica 15(2), 283–290 (2004)zbMATHGoogle Scholar
  5. 5.
    Chawla, M., Sharma, S., Sivaswamy, J., Kishore, L.: A method for automatic detection and classification of stroke from brain CT images. Eng. Med. Biol. Soc. 2009, 3581–3584 (2009)Google Scholar
  6. 6.
    Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  7. 7.
    Dutil, F., Havaei, M., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain segmentation. In: Ischemic Stroke Lesion Segmentation, p. 53 (2015)Google Scholar
  8. 8.
    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
  9. 9.
    Hasan, A., Meziane, F., Khadim, M.: Automated segmentation of tumours in MRI brain scans. In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016), pp. 55–62. SCITEPRESS (2016)Google Scholar
  10. 10.
    Erihov, M., Alpert, S., Kisilev, P., Hashoul, S.: A cross saliency approach to asymmetry-based tumor detection. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 636–643. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_76 CrossRefGoogle Scholar
  11. 11.
    Doyle, S., Vasseur, F., Dojat, M., Forbes, F.: Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM. In: Proceedings of NCI-MICCAI BraTS, pp. 18–22 (2013)Google Scholar
  12. 12.
    O’Neil, A., Murphy, S., Poole, I.: Anatomical landmark detection in CT data by learned atlas location autocontext. In: Medical Image Understanding and Analysis (MIUA), pp. 189–194 (2015)Google Scholar
  13. 13.
    Payan, A., Montana, G.: Predicting Alzheimers disease: a neuroimaging study with 3D convolutional neural networks. arXiv:1502.02506 (2015)
  14. 14.
    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
  15. 15.
    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). http://jnnp.bmj.com/content/84/9/1001 CrossRefGoogle Scholar
  16. 16.
    Dabbah, M.A., Murphy, S., Pello, H., Courbon, R., Beveridge, E., Wiseman, S., Wyeth, D., Poole, I.: Detection and location of 127 anatomical landmarks in diverse CT datasets. In: SPIE Medical Imaging, pp. 903415–903415. International Society for Optics and Photonics (2014)Google Scholar
  17. 17.
    Chollet, F.: Keras (2015). https://github.com/fchollet/keras
  18. 18.
    Theano Development Team. Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints, vol. abs/1605.02688, May 2016Google Scholar
  19. 19.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  20. 20.
    Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240 (2006)Google Scholar
  21. 21.
    Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 2843–2851 (2012)Google Scholar
  22. 22.
    Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uder, I., de Leeuw, F., Marchiori, E., van Ginneken, B., Platel, B.: Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1414–1417. IEEE (2016)Google Scholar
  23. 23.
    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). doi: 10.1007/978-3-319-24553-9_72 CrossRefGoogle Scholar
  24. 24.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations