title: An evaluation of four automatic methods of segmenting the subcortical structures in the brain.
abstract: 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.
authors.affiliation: University of Manchester, Imaging Science and Biomedical Engineering, Stopford Building, Oxford Road, Manchester M13 9PT, UK. kola.babalola@manchester.ac.uk
authors.forename: Kolawole Oluwole
authors.initials: KO
authors.lastname: Babalola
authors.forename: Brian
authors.initials: B
authors.lastname: Patenaude
authors.forename: Paul
authors.initials: P
authors.lastname: Aljabar
authors.forename: Julia
authors.initials: J
authors.lastname: Schnabel
authors.forename: David
authors.initials: D
authors.lastname: Kennedy
authors.forename: William
authors.initials: W
authors.lastname: Crum
authors.forename: Stephen
authors.initials: S
authors.lastname: Smith
authors.forename: Tim
authors.initials: T
authors.lastname: Cootes
authors.forename: Mark
authors.initials: M
authors.lastname: Jenkinson
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D002648:Child; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult