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

Authors

Dana L. Tudorascu, Department of Internal Medicine, University of PittsburghPittsburgh, PA, USA; Department of Biostatistics, University of PittsburghPittsburgh, PA, USA; Department of Psychiatry, University of PittsburghPittsburgh, PA, USA.
Helmet T. Karim, Department of Biomedical Engineering, University of Pittsburgh Pittsburgh, PA, USA.
Jacob M. Maronge, Biostatistics Program, Louisiana State University Health Sciences Center New Orleans, LA, USA.
Lea Alhilali, Department of Neuroradiology, Barrow Neurological Institute Phoenix, AZ, USA.Follow
Saeed Fakhran, Department of Radiology, Banner Health and Hospital Systems Mesa, AZ, USA.
Howard J. Aizenstein, Department of Psychiatry, University of PittsburghPittsburgh, PA, USA; Department of Biomedical Engineering, University of PittsburghPittsburgh, PA, USA.
John Muschelli, Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University Baltimore, MD, USA.
Ciprian M. Crainiceanu, Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University Baltimore, MD, USA.

Document Type

Article

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.

Publication Date

1-1-2016

Publication Title

Frontiers in neuroscience

ISSN

1662-4548

Volume

10

First Page

503

PubMed ID

27881948

Digital Object Identifier (DOI)

10.3389/fnins.2016.00503

Recommended Citation

Tudorascu, Dana L.; Karim, Helmet T.; Maronge, Jacob M.; Alhilali, Lea; Fakhran, Saeed; Aizenstein, Howard J.; Muschelli, John; and Crainiceanu, Ciprian M., "Reproducibility and Bias in Healthy Brain Segmentation: Comparison of Two Popular Neuroimaging Platforms" (2016). Neurology. 1284.
https://scholar.barrowneuro.org/neurology/1284