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

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.

Keywords: MRI reproducibility; healthy brain segmentation; segmentation bias.

Figure 1

Inferior parietal ROI is shown on a single brain (native space) for the SPM (top row) and the FSL (bottom row).

Figure 2

GM ROI's mean volumes and 95% CI are plotted. The ROI's are: anterior cingulate cortex, middle frontal gyrus, superior frontal gyrus, paracentral lobule, parietal inferior, parietal superior, postcentral, precentral, superior motor, temporal superior, respectively.

Figure 3

Good (bottom row) vs. poor (top row) GM segmentation. Red arrows indicate regions that are problematic/incorrectly classified as gray matter.

Figure 4

White matter segmentation with the areas poorly rated (top row), rated as good (middle row), and MPRAGE of the same subject (bottom row) showing the same slice white matter without any potential problems. Red arrows point to areas of potential issues.