Employing visual analytics to aid the design of white matter hyperintensity classifiers

Renata Georgia Raidou*, Hugo J. Kuijf, Neda Sepasian, Nicola Pezzotti, Willem H. Bouvy, Marcel Breeuwer, Anna Vilanova

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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,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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016
Subtitle of host publication 19th International Conference, Proceedings
PublisherSpringer-Verlag
Pages97-105
Number of pages9
ISBN (Print)9783319467221
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science
Volume9901
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
NameLecture Notes in Bioinformatics

Keywords

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

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