Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation

PLoS One. 2012;7(11):e48953. doi: 10.1371/journal.pone.0048953. Epub 2012 Nov 12.


White matter hyperintensities (WMH) on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC) of 0.96 and a mean similarity index (SI) of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN) and support vector machines (SVM) as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM), and did not need any training set.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / pathology*
  • Humans
  • Image Processing, Computer-Assisted
  • Internet
  • Magnetic Resonance Imaging / methods*
  • Reproducibility of Results
  • Software

Grant support

This work was partially supported by a PhD fellowship from the French Ministry of Higher Education and Research and by a grant from ANR (project HM-TC, number ANR-09-EMER-006). Data collection for CADASIL patients was supported by PHRC grant AOR 02-001 (DRC/APHP) and performed with the help of ARNEVA (Association de Recherche en NEurologie VAsculaire). Data collection for MCI patients was funded by Eisai as part of the “Hippocampus” study. Eisai authorized the submission of the present paper but had no role in the design and analysis of the present study nor in the preparation of the manucript. Other funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.