Docteur de l'université Automatic Segmentation and Shape Analysis ...

Chapter 4 Statistical shape model of Hippocampus 97
4.3 Experimental results
In the experiments, we first built SSMs for hippocampus based on the semi-
automated segmentations from the ADNI database. The compactness, specificity
and generalization ability of the result shape models were evaluated. Then we
tested the performance of the EM-ICP based model extrapolation method we
proposed. We compared the performance of the symmetric and asymmetric esti-
mation both with and without regularization. The reconstruction error and the
accuracy of the parameter estimation are evaluated.
4.3.1 SSM Building
The hippocampal volumes used to build the SSMs were segmented semi-automatically
by SNT provided by ADNI (see §3.4.2.1). The SSMs for right and left hippocampi
were built on a training subset consisting of 60 AD subjects with average age
75.2(6.7) years old and 60 NC subjects with average age 77.0(4.8) years old.
The correspondences of 4098 sampled landmarks in the SSMs were established
by groupwise optimization described in the §4.1.
Examples of first 3 principal components of shape variation in left hippocampus
is shown in Figure 4.11. The results of optimized SSMs are evaluated in terms
of their compactness, specificity, and generalization ability. The compactness of
the optimized SSM and the initial model is compared in Figure 4.8. For the left
hippocampus, the 90% of the total variance is explained by the first 12 modes, 95%
by the first 20 modes, and 98% by the first 36 modes in the optimized model. For
the right hippocampus, the 90% of the total variance is explained by the first 13
modes, 95% by the first 20 modes, and 98% by the first 37 modes in the optimized
model.
In the process of optimization, the information theoretic MDL is the object func-
tion to be minimized. As the MDL of the model decreases, the specificity and the
generalization ability of the model improves as shown in Figures 4.9 and 4.10.