Dissemin is shutting down on January 1st, 2025

Published in

Elsevier, NeuroImage, 3(55), p. 1091-1108, 2011

DOI: 10.1016/j.neuroimage.2010.12.067

Links

Tools

Export citation

Search in Google Scholar

Brain MAPS: An automated, accurate and robust brain extraction technique using a template library

Journal article published in 2011 by Kelvin Kk Leung, Josephine Barnes, Marc Modat, Gr Ridgway ORCID, Jw Bartlett ORCID, Nc Fox ORCID, Alzheimers Dis Neuroimaging Initia, Sébastien Ourselin, Alzheimer's Disease Neuroimaging Initiative
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

Whole brain extraction is an important pre-processing step in neuroimage analysis. Manual or semi-automated brain delineations are labour-intensive and thus not desirable in large studies, meaning that automated techniques are preferable. The accuracy and robustness of automated methods are crucial because human expertise may be required to correct any suboptimal results, which can be very time consuming. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to extract whole brains from 682 1.5 T and 157 3 T T-1-weighted MR baseline images from the Alzheimer's Disease Neuroimaging Initiative database. Semi-automated brain segmentations with manual editing and checking were used as the gold-standard to compare with the results. The median Jaccard index of MAPS was higher than HWA, BET and BSE in 1.5 land 3 T scans (p