Background:
The learning-based algorithms provide an ability to automatically estimate and refine GM, WM
and CSF. The ground truth manually achieved from the 3T MR image may not be accurate and reliable with poor image
intensity contrast. It will seriously influence the classification performance because the supervised learning-based
algorithms extremely rely on the ground truth. Recently, the 7T MR images brings about the excellent image intensity
contrast, while Structured Random Forest (SRF) performs the pixel-level classification and achieves structural and
contextual information in images.
Materials and Methods:
In this paper, a automatic segmentation algorithm is proposed
based on ground truth achieved by the corresponding 7T subjects for segmenting the 3T&1.5T brain tissues using SRF
classifiers. Through taking advantage of the 7T brain MR images, we can achieve the highly accuracy and reliable ground
truth and then implement the training of SRF classifiers. Our proposed algorithm effectively integrates the T1-weighed
images along with the probability maps to train the SRF classifiers for brain tissue segmentation.
Results:
Specifically, for
the mean Dice ratio of all 10 subjects, the proposed method achieved 95.14%±0.9%, 90.17%±1.83%, and 81.96%±
4.32% for WM, GM, and CSF. With the experiment results, the proposed algorithm can achieve better performances than
other automatic segmentation methods. Further experiments are performed on the 200 3T&1.5T brain MR images of
ADNI dataset and our proposed method shows promised performances.
Conclusions:
The authors have developed and
validated a novel fully automated method for 3T brain MR image segmentation.
Automated Electrodes Detection during simultaneous EEG/fMRI
