title: Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.
abstract: OBJECTIVE
  Segmentation 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.
  
  
  METHODS
  In 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.
  
  
  RESULTS
  The 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.
  
  
  CONCLUSIONS
  The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.
authors.affiliation: 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.
authors.forename: Minghui
authors.initials: M
authors.lastname: Deng
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China and Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
authors.forename: Renping
authors.initials: R
authors.lastname: Yu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea.
mesh_terms: D000328:Adult; D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D059629:Signal-To-Noise Ratio
keywords: 7T MRI labeling; Artificial neural networks; Biological material, e.g. blood, urine; Haemocytometers; Brain; Digital computing or data processing equipment or methods, specially adapted for specific applications; Image data processing or generation, in general; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; Learning; Magnetic resonance imaging; Medical image contrast; Medical image segmentation; Medical magnetic resonance imaging; Tissues; biomedical MRI; brain MRI; diseases; high magnetic field; image segmentation; medical image processing; pattern classification; segmentation