The Added Value of Diffusion Tensor Imaging for Automated White Matter Hyperintensity Segmentation

  • Hugo J. Kuijf
  • Chantal M. W. Tax
  • L. Karlijn Zaanen
  • Willem H. Bouvy
  • Jeroen de Bresser
  • Alexander Leemans
  • Max A. Viergever
  • Geert Jan Biessels
  • Koen L. Vincken
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Automated white matter hyperintensity (WMH) segmentation techniques for brain MRI often employ voxel-wise classifiers, trained on traditional features such as: multi-spectral MR image intensities, spatial location, texture, or shape. Recent studies show that diffusion tensor imaging (DTI) provides a measure for WMH, independent from the commonly used FLAIR images. Hence, we hypothesized that adding features derived from DTI to a voxel-wise classifier for WMH segmentation may have added value and improve segmentation results.A k nearest neighbour (kNN) classifier was implemented and trained on various combinations of features. Manual delineations of WMH were available for 20 subjects. Classifiers trained with diffusion features, such as fractional anisotropy and mean diffusivity, are compared to an equivalent classifier without diffusion features. Evaluation measures are sensitivity and Dice similarity coefficient (SI).Adding diffusion features to a kNN classifier significantly (Student’s t-test, p < 0. 0001) improved the quality of the segmentation. Depending on the chosen kNN parameters and features, improvements in sensitivity ranged from 2.4 to 13.5 % and in SI from 4.7 to 18.0 %.In conclusion, adding diffusion features derived from DTI to a voxel-wise classifier for WMH segmentation significantly improves the quality of the segmentation.

Keywords

Fractional Anisotropy Diffusion Tensor Imaging Mean Diffusivity White Matter Hyperintensity Diffusion Feature 
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.

Notes

Acknowledgements

We gratefully acknowledge the Utrecht Vascular Cognitive Impairment Study Group for recruiting the patients who were included as subjects in our study. This study was financially supported by the project Brainbox (Quantitative analysis of MR brain images for cerebrovascular disease management), funded by the Netherlands Organisation for Health Research and Development (ZonMw) in the framework of the research programme IMDI (Innovative Medical Devices Initiative); project 104002002.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hugo J. Kuijf
    • 1
  • Chantal M. W. Tax
    • 1
  • L. Karlijn Zaanen
    • 1
  • Willem H. Bouvy
    • 1
  • Jeroen de Bresser
    • 1
  • Alexander Leemans
    • 1
  • Max A. Viergever
    • 1
  • Geert Jan Biessels
    • 1
  • Koen L. Vincken
    • 1
  1. 1.University Medical Center UtrechtUtrechtThe Netherlands

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