Multi-scale Deep Convolutional Neural Network for Stroke Lesions Segmentation on CT Images

  • Liangliang Liu
  • Shuai Yang
  • Li Meng
  • Min Li
  • Jianxin WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


Ischemic stroke is the top cerebral vascular disease leading to disability and death worldwide. Accurate and automatic segmentation of lesions of stroke can assist diagnosis and treatment planning. However, manual segmentation is a time-consuming and subjective for neurologists. In this study, we propose a novel deep convolutional neural network, which is developed for the segmentation of stroke lesions from CT perfusion images. The main structure of network bases on U-shape. We embed the dense blocks into U-shape network, which can alleviate the over-fitting problem. In order to acquire more receptive fields, we use multi-kernel to divide the network into two paths, and use the dropout regularization method to achieve effective feature mapping. In addition, we use multi-scale features to obtain more spatial features, which will help improve segmentation performance. In the post-processing stage of soft segmentation, we use image median filtering to eliminate the specific noises and make the segmentation edge smoother. We evaluate our method in Ischemic Stroke Lesion Segmentations Challenge (ISLES) 2018. The results of our approach on the testing data places hight ranking.


Stroke CT perfusion images Dropout Multi-scale U-shape network 


  1. 1.
    Cardoso, M.J., Sudre, C.H., Modat, M., Ourselin, S.: Template-based multimodal joint generative model of brain data. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 17–29. Springer, Cham (2015). Scholar
  2. 2.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  3. 3.
    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). Scholar
  4. 4.
    Fakhry, A., Zeng, T., Ji, S.: Residual deconvolutional networks for brain electron microscopy image segmentation. IEEE Trans. Med. Imaging 36(2), 447–456 (2017)CrossRefGoogle Scholar
  5. 5.
    Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel MR images. Neuroimage 57(2), 378–390 (2011)CrossRefGoogle Scholar
  6. 6.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)Google Scholar
  7. 7.
    Gonzalez, R.G., Hirsch, J.A., Koroshetz, W.J., Lev, M.H., Schaefer, P.: Acute ischemic stroke: imaging and intervention. J. Neuroradiol. 33(3), 193 (2006)CrossRefGoogle Scholar
  8. 8.
    Grimaud, J., et al.: Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. Magn. Reson. Imaging 14(5), 495–505 (1996)CrossRefGoogle Scholar
  9. 9.
    Guerrero, R., et al.: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage: Clin. 17(C), 918–934 (2017)Google Scholar
  10. 10.
    Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)Google Scholar
  11. 11.
    Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging 22(8), 951–958 (2003)CrossRefGoogle Scholar
  12. 12.
    Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)CrossRefGoogle Scholar
  13. 13.
    Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261–2269 (2017)Google Scholar
  14. 14.
    Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Trans. Acoust. Speech Signal Process. 27(1), 13–18 (1979)CrossRefGoogle Scholar
  15. 15.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.A.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)CrossRefGoogle Scholar
  16. 16.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167v3
  17. 17.
    Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61 (2016)CrossRefGoogle Scholar
  18. 18.
    Lcun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  19. 19.
    Ledig, C., et al.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40 (2015)CrossRefGoogle Scholar
  20. 20.
    Li, X., et al.: 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images. Med. Image Anal. 45, 41–54 (2018)CrossRefGoogle Scholar
  21. 21.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  22. 22.
    Maier, O., et al.: ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)CrossRefGoogle Scholar
  23. 23.
    Rao, A., Ledig, C., Newcombe, V., Menon, D., Rueckert, D.: Contusion segmentation from subjects with traumatic brain injury: a random forest framework. In: IEEE International Symposium on Biomedical Imaging, pp. 333–336 (2014)Google Scholar
  24. 24.
    Rekik, I., Allassonnire, S., Carpenter, T.K., Wardlaw, J.M.: Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage: Clin. 1(1), 164–178 (2012)CrossRefGoogle Scholar
  25. 25.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  26. 26.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  28. 28.
    Zhang, R., et al.: Automatic segmentation of acute ischemic stroke from DWI using 3D fully convolutional denseNets. IEEE Trans. Med. Imaging, 1 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Liangliang Liu
    • 1
  • Shuai Yang
    • 2
  • Li Meng
    • 2
  • Min Li
    • 1
  • Jianxin Wang
    • 1
    Email author
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Department of Radiology, Xiangya HospitalCentral South UniversityChangshaPeople’s Republic of China

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