• Minghui Deng, Renping Yu, Li Wang, Feng Shi, Pew-Thian Yap, Dinggang Shen, and Alzheimer's Disease Neuroimaging Initiative.
• College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China and Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
• Med Phys. 2016 Dec 1; 43 (12): 6588-6597.

PurposeSegmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio.MethodsIn this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation.ResultsThe proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values < 0.021). For the ADNI dataset, the group difference comparisons indicate that the proposed algorithm outperforms state-of-the-art segmentation methods.ConclusionsThe authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.© 2016 American Association of Physicists in Medicine.

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