Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers

  • Renata Georgia RaidouEmail author
  • Hugo J. Kuijf
  • Neda Sepasian
  • Nicola Pezzotti
  • Willem H. Bouvy
  • Marcel Breeuwer
  • Anna Vilanova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end, classifiers are often trained – usually, using T1 and FLAIR weighted MR images. Incorporating additional features, derived from diffusion weighted MRI, could improve classification. However, the multitude of diffusion-derived features requires selecting the most adequate. For this, automated feature selection is commonly employed, which can often be sub-optimal. In this work, we propose a different approach, introducing a semi-automated pipeline to select interactively features for WMH classification. The advantage of this solution is the integration of the knowledge and skills of experts in the process. In our pipeline, a Visual Analytics (VA) system is employed, to enable user-driven feature selection. The resulting features are T1, FLAIR, Mean Diffusivity (MD), and Radial Diffusivity (RD) – and secondarily, \(C_S\) and Fractional Anisotropy (FA). The next step in the pipeline is to train a classifier with these features, and compare its results to a similar classifier, used in previous work with automated feature selection. Finally, VA is employed again, to analyze and understand the classifier performance and results.


White matter hyperintensities (WMHs) Visual Analytics (VA) Classification Interactive feature selection 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Renata Georgia Raidou
    • 1
    • 2
    Email author
  • Hugo J. Kuijf
    • 3
  • Neda Sepasian
    • 1
  • Nicola Pezzotti
    • 2
  • Willem H. Bouvy
    • 4
  • Marcel Breeuwer
    • 1
    • 5
  • Anna Vilanova
    • 1
    • 2
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands
  3. 3.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  4. 4.Department of Neurology, Brain Center Rudolf MagnusUniversity Medical Center UtrechtUtrechtThe Netherlands
  5. 5.Philips HealthcareBestThe Netherlands

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