Reproducibility and bias in healthy brain segmentation: Comparison of two popular neuroimaging platforms

Dana L. Tudorascu, Helmet T. Karim, Jacob M. Maronge, Lea Alhilali, Saeed Fakhran, Howard J. Aizenstein, John Muschelli, Ciprian M. Crainiceanu

Research output: Contribution to journalArticlepeer-review

Abstract

We evaluated and compared the performance of two popular neuroimaging processing platforms: Statistical Parametric Mapping (SPM) and FMRIB Software Library (FSL). We focused on comparing brain segmentations using Kirby21, a magnetic resonance imaging (MRI) replication study with 21 subjects and two scans per subject conducted only a few hours apart. We tested within- and between-platform segmentation reliability both at the whole brain and in 10 regions of interest (ROIs). For a range of fixed probability thresholds we found no differences between-scans within-platform, but large differences between-platforms. We have also found very large differences between- and within-platforms when probability thresholds were changed. A randomized blinded reader study indicated that: (1) SPM and FSL performed well in terms of gray matter segmentation; (2) SPM and FSL performed poorly in terms of white matter segmentation; and (3) FSL slightly outperformed SPM in terms of CSF segmentation. We also found that tissue class probability thresholds can have profound effects on segmentation results. We conclude that the reproducibility of neuroimaging studies depends on the neuroimaging software-processing platform and tissue probability thresholds. Our results suggest that probability thresholds may not be comparable across platforms and consistency of results may be improved by estimating a probability threshold correspondence function between SPM and FSL.

Original languageEnglish (US)
Article number503
JournalFrontiers in Neuroscience
Volume10
Issue numberNOV
DOIs
StatePublished - 2016

Keywords

  • Healthy brain segmentation
  • MRI reproducibility
  • Segmentation bias

ASJC Scopus subject areas

  • Neuroscience(all)

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