Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3495958?pdf=render |
| _version_ | 1818325147025670144 |
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| author | Thomas Samaille Ludovic Fillon Rémi Cuingnet Eric Jouvent Hugues Chabriat Didier Dormont Olivier Colliot Marie Chupin |
| author_facet | Thomas Samaille Ludovic Fillon Rémi Cuingnet Eric Jouvent Hugues Chabriat Didier Dormont Olivier Colliot Marie Chupin |
| author_sort | Thomas Samaille |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T11:39:51Z |
| format | Article |
| id | doaj.art-05ebca3326644d7987ed3946e295d9f6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T11:39:51Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj.art-05ebca3326644d7987ed3946e295d9f62022-12-21T23:47:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-01711e4895310.1371/journal.pone.0048953Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.Thomas SamailleLudovic FillonRémi CuingnetEric JouventHugues ChabriatDidier DormontOlivier ColliotMarie ChupinWhite 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.http://europepmc.org/articles/PMC3495958?pdf=render |
| spellingShingle | Thomas Samaille Ludovic Fillon Rémi Cuingnet Eric Jouvent Hugues Chabriat Didier Dormont Olivier Colliot Marie Chupin Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS ONE |
| title | Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. |
| title_full | Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. |
| title_fullStr | Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. |
| title_full_unstemmed | Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. |
| title_short | Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. |
| title_sort | contrast based fully automatic segmentation of white matter hyperintensities method and validation |
| url | http://europepmc.org/articles/PMC3495958?pdf=render |
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