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.
Highlights
Magnetic Resonance Imaging (MRI) is widely used in clinical practice and research
Our results indicate that: (1) Statistical Parametric Mapping (SPM) and FMRIB Software Library (FSL) provide results that exhibit moderate to large differences indicating differences between the two software platforms; (2) there is no statistically significant scan effect; and (3) there is a statistically significant segmentation method effect: significant differences were detected between the two segmentation methods for gray matter and white matter at all probability thresholds considered and for cerebrospinal fluid at two of the three thresholds
The white matter volume computed using FSL was lower than that computed with SPM at all thresholds, except for the 0.5 probability threshold
Summary
Magnetic Resonance Imaging (MRI) is widely used in clinical practice and research. The current state-of-the-art for pre-processing MRI data is to use standard software packages and develop research-group-specific processing pipelines. We focus on studying the reproducibility and bias. Reproducibility and Bias in Healthy Brain Segmentation of brain MRI segmentation software. Ox.ac.uk/fsl/index.html), and compare results on the Kirby dataset (Landman et al, 2011). Kirby is a publicly available dataset containing scan-rescan imaging sessions on 21 healthy volunteers with no history of neurological disorders. Multiple imaging modalities were acquired on these volunteers including a three-dimensional, T1-weighted, gradient-echo sequence (MPRAGE), fluid attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), resting state functional magnetic resonance imaging (fMRI), B0, and B1 field maps. For the purpose of this paper we use only the MPRAGE structural images
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