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

R.G. Raidou, H.J. Kuijf, N. Sepasian, N. Pezzotti, W.H. Bouvy, M. Breeuwer, A. Vilanova

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

5 Citations (Scopus)
2 Downloads (Pure)

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 publication19th International Conference, Athens, Greece, October 17-21, 2016 : Proceedings
EditorsS. Ourselin, L. Joskowicz, M.R. Sabuncu, G. Unal, W. Wells
PublisherSpringer
Pages97-105
Volume2
ISBN (Electronic)978-3-319-46723-8
ISBN (Print)978-3-319-46722-1
DOIs
Publication statusPublished - 2016

Publication series

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

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