Automatic identification of the reference system based on the fourth ventricular landmarks in T1-weighted MR images.
OBJECTIVE
The reference system based on the fourth ventricular landmarks (including the fastigial point and ventricular floor plane) is used in medical image analysis of the brain stem. The objective of this study was to develop a rapid, robust, and accurate method for the automatic identification of this reference system on T1-weighted magnetic resonance images.


METHODS
The fully automated method developed in this study consisted of four stages: preprocessing of the data set, expectation-maximization algorithm-based extraction of the fourth ventricle in the region of interest, a coarse-to-fine strategy for identifying the fastigial point, and localization of the base point. The method was evaluated on 27 Brain Web data sets qualitatively and 18 Internet Brain Segmentation Repository data sets and 30 clinical scans quantitatively.


RESULTS
The results of qualitative evaluation indicated that the method was robust to rotation, landmark variation, noise, and inhomogeneity. The results of quantitative evaluation indicated that the method was able to identify the reference system with an accuracy of 0.7 +/- 0.2 mm for the fastigial point and 1.1 +/- 0.3 mm for the base point. It took <6 seconds for the method to identify the related landmarks on a personal computer with an Intel Core 2 6300 processor and 2 GB of random-access memory.


CONCLUSIONS
The proposed method for the automatic identification of the reference system based on the fourth ventricular landmarks was shown to be rapid, robust, and accurate. The method has potentially utility in image registration and computer-aided surgery.
Fu
Yili
Y
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Nangang District, Harbin, Heilongjiang, China.
Gao
Wenpeng
W
Chen
Xiaoguang
X
Zhu
Minwei
M
Shen
Weigao
W
Wang
Shuguo
S
D000465:Algorithms; D001185:Artificial Intelligence; D002681:China; D020546:Fourth Ventricle / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D010363:Pattern Recognition, Automated / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity