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Babalola, K.O.* ; Patenaude, B.* ; Aljabar, P.* ; Schnabel, J.A.* ; Kennedy, D.* ; Crum, W.R.* ; Smith, S.* ; Cootes, T.* ; Jenkinson, M.* ; Rueckert, D.*

An evaluation of four automatic methods of segmenting the subcortical structures in the brain.

Neuroimage 47, 1435-1447 (2009)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
The automation of segmentation of subcortical structures in the brain is an active research area. We have comprehensively evaluated four novel methods of fully automated segmentation of subcortical structures using volumetric, spatial overlap and distance-based measures. Two methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a brain atlas (EMS), and two incorporate statistical models of shape and appearance - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed better than the others according to all three classes of metrics. In summary over all structures, the ranking by the Dice coefficient was CFL, BAM, joint EMS and PAM. The Hausdorff distance ranked the methods as CFL, joint PAM and BAM, EMS, whilst percentage absolute volumetric difference ranked them as joint CFL and PAM, joint BAM and EMS. Furthermore, as we had four methods of performing segmentation, we investigated whether the results obtained by each method were more similar to each other than to the manual segmentations using Williams' Index. Reassuringly, the Williams' Index was close to 1 for most subjects (mean = 1.02, sd = 0.05), indicating better agreement of each method with the gold standard than with the other methods. However, 2% of cases (mainly amygdala and nucleus accumbens) had values outside 3 standard deviations of the mean. © 2009 Elsevier Inc. All rights reserved.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 1053-8119
e-ISSN 1095-9572
Journal NeuroImage - a Journal of Brain Function
Quellenangaben Volume: 47, Issue: 4, Pages: 1435-1447 Article Number: , Supplement: ,
Publisher Elsevier
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)