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.
Conclusion: The authors have developed and validated a novel fully automated method for 3T
brain MR image segmentation.