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 1.5T and 157 3T 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 1.5T and 3T scans (p<0.05, all tests), and the 1st to 99th centile range of the Jaccard index of MAPS was smaller than HWA, BET and BSE in 1.5T and 3T scans ( p<0.05, all tests). HWA and MAPS were found to be best at including all brain tissues (median false negative rate ≤0.010% for 1.5T scans and ≤0.019% for 3T scans, both methods). The median Jaccard index of MAPS were similar in both 1.5T and 3T scans, whereas those of BET, BSE and HWA were higher in 1.5T scans than 3T scans (p<0.05, all tests). We found that the diagnostic group had a small effect on the median Jaccard index of all four methods. In conclusion, MAPS had relatively high accuracy and low variability compared to HWA, BET and BSE in MR scans with and without atrophy.
"Beyond the standardization of methods and data sets, MRI studies carried out with the ADNI cohort have impacted clinical trials in a number of ways. Fox and coworkers developed improved methods for measuring the rate of atrophy across multiple sites and for reducing required sample sizes   , and also developed automated methods to measure brain and hippocampal volume and rates of atrophy   . These have been incorporated into large commercial clinical trials and submitted to the European Medicines Agency, leading to guidance on hippocampal volume measurement in trials . "
"In the context of segmentation of structural human brain MRI, multiatlas techniques have also been applied to preprocessing tasks such as skull stripping (Leung et al., 2011; Weisenfeld and Warfield, 2011b) and tissue classification (Bouix et al., 2007; Crum, 2009), as well as to the segmentation of tumors (Wang and Yushkevich, 2013b; Warfield et al., 2004). The multi-atlas approach has been employed for the segmentation of cortical and subcortical structures in MRI data from neonates and infants, too (Gholipour et al., 2012; Gousias et al., 2008, 2010, 2013; Shi et al., 2010; Wang et al., 2014b), in which the contrast inversion due to the ongoing myelination complicates the segmentation. "
"International Journal of Biomedical Imaging by using nonlinear registration atlas-based approaches  . More recent works of special interest for the brain extraction problem are methods like MAPS  and BEaST . Both methods rely on the application of a multiatlas label fusion strategy. "
[Show abstract][Hide abstract] ABSTRACT: Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.
International Journal of Biomedical Imaging 09/2014; 2014:820205. DOI:10.1155/2014/820205
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