Reproducibility and Bias in Healthy Brain Segmentation: Comparison of Two Popular Neuroimaging Platforms

• Published in: Frontiers in neuroscience Vol. 10; p. 503
• Main Authors: Tudorascu, Dana L., Karim, Helmet T., Maronge, Jacob M., Alhilali, Lea, Fakhran, Saeed, Aizenstein, Howard J., Muschelli, John, Crainiceanu, Ciprian M.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 09.11.2016; Frontiers Media S.A
• Subjects: Accuracy; Algorithms; Automation; Bias; Brain mapping; Brain research; Cerebrospinal fluid; Computer programs; Datasets; Health care; healthy brain segmentation; Image processing; Investigations; Magnetic resonance imaging; Medical imaging; Methods; MRI reproducibility; Neuroimaging; neuroimaging platforms comparison; Neuroscience; NMR; Nuclear magnetic resonance; Reproducibility; Segmentation; segmentation bias; Software; SPM and FSL segmentation differences; Substantia alba; Substantia grisea; United States > US; Pittsburgh Pennsylvania; MRI reproducibility; healthy brain segmentation; segmentation bias
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2016.00503

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.