Advertisement

V-Net and U-Net for Ischemic Stroke Lesion Segmentation in a Small Dataset of Perfusion Data

  • Gustavo Retuci PinheiroEmail author
  • Raphael Voltoline
  • Mariana Bento
  • Leticia Rittner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

Ischemic stroke is the result of an obstruction within a brain blood vessel, blocking the fresh blood flow, resulting in a tissue lesion. Early prediction of the ischemic stroke lesion region is important because it can help to choose the most suitable treatment. However, that is not trivial since current medical data, such as CT and MRI, have no explicit information about the future extension of the permanent lesion. A step towards efficiently using these data to predict the lesions is the use of Deep Convolutional Neural Networks as they are able to extract “hidden” information from the data when a reasonable labeled dataset is available and the deep networks are used properly. In order to try to extract this information, we have tested two different deep network architectures that are the state of the art in segmentation problems: V-net and U-net. In both networks, we tried different configurations, such as depth variations, pixel interpolations, MRI image combinations, among others. Experiments showed the following: normalizing the voxels sizes results in better training and predictions; deeper U-Net performs slightly better than the shallower U-Net, however it requires much more computation for only a small gain in accuracy; the inclusion of CT modality improved slightly the results; the use of only perfusion maps brought much better results than the use of raw perfusion data; smaller lesions are harder to detect properly.

Keywords

Ischemic stroke Lesion segmentation Deep learning U-Net V-Net Perfusion 

References

  1. 1.
    Vernooij, M.W., et al.: Incidental findings on brain MRI in the general population. N. Engl. J. Med. 357(18), 1821–1828 (2007).  https://doi.org/10.1056/NEJMoa070972CrossRefGoogle Scholar
  2. 2.
    Ginsberg, M.D.: Neuroprotection for ischemic stroke: past, present and future. Neuropharmacology 55, 363–389 (2008). PubMedCrossRefGoogle Scholar
  3. 3.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of 2016 4th International Conference on 3D Vision, 3DV, pp. 565–571 (2016)Google Scholar
  4. 4.
    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).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  5. 5.
    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).  https://doi.org/10.1016/j.media.2016.07.009CrossRefGoogle Scholar
  6. 6.
    Kistler, M., et al.: The virtual skeleton database: an open access repository for biomedical research and collaboration. JMIR 15(11), e245 (2013).  https://doi.org/10.2196/jmir.2930CrossRefGoogle Scholar
  7. 7.
    Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. ArXiv e-prints (2009)Google Scholar
  8. 8.
    Hinton, G., Srivastava, N., Swersky K.: Neural networks for machine learning. http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf. Accessed 10 Oct 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gustavo Retuci Pinheiro
    • 1
    Email author
  • Raphael Voltoline
    • 1
  • Mariana Bento
    • 2
  • Leticia Rittner
    • 1
  1. 1.School of Electrical and Computing Engineering (FEEC)University of Campinas (UNICAMP)CampinasBrazil
  2. 2.Calgary Image Processing and Analysis Centre (CIPAC), Department of Radiology and Clinical NeuroscienceUniversity of CalgaryCalgaryCanada

Personalised recommendations