T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions—an MRI evaluation study
Development of a warp-based automated brain segmentation approach of 3D fluid-attenuated inversion recovery (FLAIR) images and comparison to 3D T1-based segmentation.
3D FLAIR and 3D T1-weighted sequences of 30 healthy subjects (mean age 29.9 ± 8.3 years, 8 female) were acquired on the same 3T MR scanner. Warp-based segmentation was applied for volumetry of total gray matter (GM), white matter (WM), and 116 atlas regions. Segmentation results of both sequences were compared using Pearson correlation (r).
Correlation of GM segmentation results based on FLAIR and T1 was overall good for cortical structures (mean r across all cortical structures = 0.76). Comparatively weaker results were found in the occipital lobe (r = 0.77), central region (mean r = 0.58), basal ganglia (mean r = 0.59), thalamus (r = 0.30), and cerebellum (r = 0.73). FLAIR segmentation underestimated volume of the central region compared to T1, but showed a better anatomic concordance with the occipital lobe on visual review and subcortical structures, when also compared to manual segmentation. Visual analysis of FLAIR-based WM segmentation revealed frequent misclassification of regions of high signal intensity as GM.
Warp-based FLAIR segmentation yields comparable results to T1 segmentation for most cortical GM structures and may provide anatomically more congruent segmentation of subcortical GM structures. Selected cortical regions, especially the central region and total WM, seem to be underestimated on FLAIR segmentation.
KeywordsMagnetic resonance imaging Brain Neuroanatomy Cohort studies
Fluid-attenuated inversion recovery
Automatic Anatomical Labeling
Functional magnetic resonance imaging of the brain (FMRIB) software library
FMRIB’s Automated Segmentation Tool
Analyses of functional images
Montreal Neurological Institute
FMRIB’s linear image registration tool
FMRIB’s nonlinear image registration tool
Compliance with ethical standards
No funding was received for this study.
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 1.Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JLR, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19:1523–1536CrossRefGoogle Scholar
- 2.Bamberg F, Kauczor HU, Weckbach S, Schlett CL, Forsting M, Ladd SC, Greiser KH, Weber MA, Schulz-Menger J, Niendorf T, Pischon T, Caspers S, Amunts K, Berger K, Bülow R, Hosten N, Hegenscheid K, Kröncke T, Linseisen J, Günther M, Hirsch JG, Köhn A, Hendel T, Wichmann HE, Schmidt B, Jöckel KH, Hoffmann W, Kaaks R, Reiser MF, Völzke H, For the German National Cohort MRI Study Investigators (2015) Whole-body MR imaging in the German National Cohort: rationale, design, and technical background. Radiology 277:206–220CrossRefGoogle Scholar
- 4.Lindig T, Kotikalapudi R, Schweikardt D et al (2017) Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.02.016