A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation

  • Chen Qin
  • Ricardo Guerrero Moreno
  • Christopher Bowles
  • Christian Ledig
  • Philip Scheltens
  • Frederik Barkhof
  • Hanneke Rhodius-Meester
  • Betty Tijms
  • Afina W. Lemstra
  • Wiesje M. van der Flier
  • Ben Glocker
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation.

Keywords

Dementia With Lewy Body White Matter Hyperintensities Label Data Subjective Memory Complaint Label Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Anbeek, P., Vincken, K.L., van Osch, M.J., Bisschops, R.H., van der Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21(3), 1037–1044 (2004)CrossRefGoogle Scholar
  2. 2.
    Caligiuri, M.E., Perrotta, P., Augimeri, A., Rocca, F., Quattrone, A., Cherubini, A.: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review. Neuroinformatics 13(3), 261–276 (2015)CrossRefGoogle Scholar
  3. 3.
    Fazekas, F., Chawluk, J.B., Alavi, A., Hurtig, H.I., Zimmerman, R.A.: MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am. J. Neuroradiol. 8(3), 421–426 (1987)Google Scholar
  4. 4.
    Gibson, E., Gao, F., Black, S.E., Lobaugh, N.J.: Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T. J. Magn. Reson. Imaging 31(6), 1311–1322 (2010)CrossRefGoogle Scholar
  5. 5.
    Ithapu, V., Singh, V., Lindner, C., Austin, B.P., Hinrichs, C., Carlsson, C.M., Bendlin, B.B., Johnson, S.C.: Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies. Hum. Brain Mapp. 35(8), 4219–4235 (2014)Google Scholar
  6. 6.
    Kikinis, R., Guttmann, C.R., Metcalf, D., Wells, W.M., Ettinger, G.J., Weiner, H.L., Jolesz, F.A.: Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. J. Magn. Reson. Imaging 9(4), 519–530 (1999)CrossRefGoogle Scholar
  7. 7.
    Lao, Z., Shen, D., Liu, D., Jawad, A.F., Melhem, E.R., Launer, L.J., Bryan, R.N., Davatzikos, C.: Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad. Radiol. 15(3), 300–313 (2008)CrossRefGoogle Scholar
  8. 8.
    Ledig, C., Heckemann, R.A., Hammers, A., Lopez, J.C., Newcombe, V.F., Makropoulos, A., Lötjönen, J., Menon, D.K., Rueckert, D.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40–58 (2015)CrossRefGoogle Scholar
  9. 9.
    Liu, W., Hua, G., Smith, J.: Unsupervised one-class learning for automatic outlier removal. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3826–3833 (2014)Google Scholar
  10. 10.
    Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V.J., Zimmer, C., et al.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4), 3774–3783 (2012)CrossRefGoogle Scholar
  11. 11.
    Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)CrossRefGoogle Scholar
  12. 12.
    Wardlaw, J.M., Smith, E.E., Biessels, G.J., Cordonnier, C., Fazekas, F., Frayne, R., Lindley, R.I., T O’Brien, J., Barkhof, F., Benavente, O.R., et al.: Neuroimaging standards for research into small vessel disease and its contribution to aging and neurodegeneration. Lancet Neurol. 12(8), 822–838 (2013)CrossRefGoogle Scholar
  13. 13.
    Wu, M., Ye, J.: A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2088–2092 (2009)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Yang, F., Shan, Z.Y., Kruggel, F.: White matter lesion segmentation based on feature joint occurrence probability and \(\chi \)2 random field theory from magnetic resonance (MR) images. Pattern Recogn. Lett. 31(9), 781–790 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Chen Qin
    • 1
  • Ricardo Guerrero Moreno
    • 1
  • Christopher Bowles
    • 1
  • Christian Ledig
    • 1
  • Philip Scheltens
    • 2
  • Frederik Barkhof
    • 2
  • Hanneke Rhodius-Meester
    • 2
  • Betty Tijms
    • 2
  • Afina W. Lemstra
    • 2
  • Wiesje M. van der Flier
    • 2
  • Ben Glocker
    • 1
  • Daniel Rueckert
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Department of NeurologyVU University Medical CenterAmsterdamThe Netherlands

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