Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI.

K Babalola, B Patenaude, P Aljabar, J Schnabel, D Kennedy, W Crum, S Smith, TF Cootes, M Jenkinson, D Rueckert

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - 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 significantly better than the other three methods according to all three classes of metrics.
    Original languageEnglish
    Title of host publicationMed Image Comput Comput Assist Interv Int Conf
    Place of PublicationGermany
    Volume11( Pt 1)
    Publication statusPublished - 2008
    EventMICCAI - London
    Duration: 1 Jan 1824 → …


    Period1/01/24 → …


    • Algorithms
    • Artificial Intelligence
    • pathology: Brain
    • diagnosis: Brain Diseases
    • pathology: Cerebral Cortex
    • Humans
    • methods: Image Enhancement
    • methods: Image Interpretation, Computer-Assisted
    • methods: Magnetic Resonance Imaging
    • methods: Pattern Recognition, Automated
    • Reproducibility of Results
    • Sensitivity and Specificity


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