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Abstract
Accurate classification and quantification of brain tissues is important for monitoring disease progression, measurement of atrophy, and correlating magnetic resonance (MR) measures with clinical disability. Classification of MR brain images in the presence of lesions, such as multiple sclerosis (MS), is particularly challenging. Images obtained with lower resolution often suffer from partial volume averaging leading to false classifications. While partial volume averaging can be reduced by acquiring volumetric images at high resolution, image segmentation and quantification can be technically challenging. In this study, we integrated the brain anatomical knowledge with non-parametric and parametric statistical classifiers for automatically classifying tissues and lesions on high resolution multichannel three-dimensional images acquired on 60 MS brains. The results of automatic lesion segmentation were reviewed by the expert. The agreement between results obtained by the automated analysis and the expert was excellent as assessed by the quantitative metrics, low absolute volume difference percent (36.18 ± 34.90), low average symmetric surface distance (1.64 mm ± 1.30 mm), high true positive rate (84.75 ± 12.69), and low false positive rate (34.10 ± 16.00). The segmented results were also in close agreement with the corrected results as assessed by Bland–Altman and regression analyses. Finally, our lesion segmentation was validated using the MS lesion segmentation grand challenge dataset (MICCAI 2008).
Highlights
Magnetic resonance imaging (MRI) plays a major role in quantifying brain lesions and other tissues for assessing the disease course, understanding the underlying pathophysiology, and investigating the therapeutic efficacy in multiple sclerosis (MS)
The arrows in this figure indicate the false classifications following the application of the Parzen and EM-hidden Markov random field (HMRF) classifications
Corrected images included in the figure indicate true positive, false positive, and false negative lesion classifications obtained with the segmentation technique
Summary
Magnetic resonance imaging (MRI) plays a major role in quantifying brain lesions and other tissues for assessing the disease course, understanding the underlying pathophysiology, and investigating the therapeutic efficacy in multiple sclerosis (MS). On MRI, T2 hyperintense white matter (WM) (THWLs), T1 hypointense (or black holes), Gd-enhanced, and cortical lesions are seen. Both lesion volumes and/or number and their locations appear to influence the clinical disability, including cognitive impairment (Bodini et al, 2011; Fisniku et al, 2008; Kincses et al, 2011; Mostert et al, 2010; Patti et al, 2009; Poonawalla et al, 2010; Rudick et al, 2006; Sastre-Garriga and Tintore, 2010; Tao et al, 2009a; Vellinga et al, 2009). Improper tissue classification could affect measures such as GM and WM atrophy which appear to correlate with clinical disability (Chard et al, 2010; Derakhshan et al, 2010; Grassiot et al, 2009)
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