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

Conference paper (2016)

Authors

R.G. Raidou Computer Graphics and Visualisation - EEMCS
Hugo J. Kuijf University Medical Center Utrecht
Neda Sepasian Eindhoven University of Technology
N. Pezzotti Computer Graphics and Visualisation - EEMCS
Willem H. Bouvy University Medical Center Utrecht
Marcel Breeuwer Philips Healthcare, Eindhoven University of Technology
A. Vilanova Bartroli Eindhoven University of Technology, Computer Graphics and Visualisation - EEMCS
Research Group
Computer Graphics and Visualisation (EEMCS) (TU Delft)
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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Computer Graphics and Visualisation

Abstract

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  CS

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.