Journal
NEUROIMAGE
Volume 45, Issue 4, Pages 1151-1161Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.01.011
Keywords
MRI; Brain tissue segmentation; White matter lesions; White matter hyperintensities
Funding
- Erasmus Medical Center and Erasmus University Rotterdam
- Netherlands Organization for Scientific Research (NWO) [948-00-010, 918-46-615]
- Netherlands Organization for Health Research and Development (ZonMw)
- Research Institute for Diseases in the Elderly (RIDE)
- Ministry of Education, Culture and Science
- Ministry of Health, Welfare and Sports
- European Commission (DG XII)
- Municipality of Rotterdam
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A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations. (C) 2009 Elsevier Inc. All rights reserved.
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