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. 2014 Mar 8;4(1):13-26.
eCollection 2014 Mar.

Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

K Kazemi  1 , N Noorizadeh  1
Affiliations

Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

K Kazemi et al. J Biomed Phys Eng. .

Abstract

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is needed for the neuroimaging applications.

Methods: In this paper, performance evaluation of three widely used brain segmentation software packages SPM8, FSL and Brainsuite is presented. Segmentation with SPM8 has been performed in three frameworks: i) default segmentation, ii) SPM8 New-segmentation and iii) modified version using hidden Markov random field as implemented in SPM8-VBM toolbox.

Results: The accuracy of the segmented GM, WM and CSF and the robustness of the tools against changes of image quality has been assessed using Brainweb simulated MR images and IBSR real MR images. The calculated similarity between the segmented tissues using different tools and corresponding ground truth shows variations in segmentation results.

Conclusion: A few studies has investigated GM, WM and CSF segmentation. In these studies, the skull stripping and bias correction are performed separately and they just evaluated the segmentation. Thus, in this study, assessment of complete segmentation framework consisting of pre-processing and segmentation of these packages is performed. The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.

Keywords: Brain; Brainsuite; FSL; MRI; SPM; Segmentation.

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Figures

Figure 1
Figure 1
The segmented GM, WM and CSF from simulated MR image with n=0% and rf=0%.  The first row shows the input MR image and the ground truth for GM, WM and CSF. The second row from left to right shows the segmentation results using SPM8-Seg, SPM8-VBM, SPM8-NewSeg, FSL and Brainsuite, respectively.
Figure 2
Figure 2
Quantitative evaluation of the segmented GM and WM from simulated MR images based on Dice similarity metric using three packages: SPM, FSL and Brainsuite.
Figure 3
Figure 3
The segmented GM, WM and CSF from a selected subject from IBSR real MR images.  The first row shows the input MR image and the ground truth for GM, WM and CSF. The second row from left to right shows the segmentation results using SPM8-Seg, SPM8-VBM, SPM8-NewSeg, FSL and Brainsuite, respectively.
Figure 4
Figure 4
Dice similarity metric for segmented WM and GM from IBSR real MR images
Figure 5
Figure 5
Dice similarity metric for segmented Brain from Brainwebsimulated MR images.
Figure 6
Figure 6
Dice similarity metric for segmented Brain from IBSR real MR images.
See this image and copyright information in PMC

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