Learning‐based 3T brain MRI segmentation with guidance from 7T MRI labeling. Issue 12 (21st November 2016)

• Record Type: Journal Article
• Authors: Deng, Minghui; Yu, Renping; Wang, Li; Shi, Feng; Yap, Pew‐Thian; Shen, Dinggang
• Is Part Of: Medical physics. Volume 43:Issue 12(2016)
• Page Start: 6588
• Page End: 6597
• Publication Date: 2016-11-21
• Subjects: biomedical MRI -- diseases -- image segmentation -- medical image processing -- pattern classification; Magnetic resonance imaging; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; segmentation -- brain MRI -- 7T MRI labeling -- high magnetic field; Medical magnetic resonance imaging -- Medical image segmentation -- Brain -- Tissues -- Learning -- Medical image contrast -- Artificial neural networks; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4967487
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: 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.