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

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118 Chapter 5 Quantitative shape analysis of hippocampus in AD The data set used to train the classifier was resampled with replacement for 25 times, and the ensemble of classifiers consisted of SVMs with RBF kernel each of which were trained on the resampled data. The cost parameter C and the scale parameter of the kernel γ are not explicit parameters for the ensemble classifier, but parameters of individual SVMs in the ensemble. For each individual SVM, both C and gamma are tuned by a grid search over the range C = 2 −7 , 2 −6 , . . . , 2 2 , 2 3 and γ = 2 −2 , 2 −1 , . . . , 2 7 , 2 8 using the resampled training set with 10-fold cross validation. The parameters giving the best performance on the resampled training data are chosen for the SVM. Each SVM in the ensemble may have different values of C and gamma parameters. The predictions of 25 SVMs were combined by a voting rule to produce the final output. The validation on the training set with varying the threshold α, and experiments using separated training and testing sets were performed. We use the out-of-bag estimation (OOB, Breiman, 1996b) to evaluate the performance of the bagging classifier on the training set. For each resample of the training data with replacement, there are approximately one-third of the training data left out of the bootstrap resampling. A classification of each left-out (i.e. ‘out-of-bag’) case can be obtained from the SVM trained using the selected (i.e. ‘in-bag’) sample. The classification of each case in the training set can be de- termined by a voting rule after multiple runs of resampling. The OOB estimation can be obtained from average accuracy on the left-out data. In our experiment, the OOB estimates are carried out by 70 resamplings, which result in each training case being left out and classified for approximately 25 times on average. In order to compare with the disease classification result reported by Cuingnet et al. (2010) using hippocampal shape information (Gerardin et al., 2009), experiments using the linear C-SVM on SSM features were also carried out. The cost parameter C was chosen by searching over the range 10 −5 , 10 −4.5 , . . . , 10 2.5 , 10 3 and leave-one-out cross validation on the training set, in the same manner as in Cuingnet et al. (2010).
Chapter 5 Quantitative shape analysis of hippocampus in AD 119 5.4 Correlating the shape variation with memory performance Hippocampus plays a critical role in memory formation, and its atrophy is as- sociated with memory decline in AD (Heun et al., 1997; Petersen et al., 2000). We assessed our global and local hippocampal SSMs in terms of their relevance to memory decline by the correlation analysis between the shape descriptors de- rived from these models and the scores from memory tests. The results from Logical Memory test (immediate and delayed recall), Auditory Verbal Learning Test (AVLT, 30-minute delay, Ivnik et al., 1992), and the memory subscores of Alzheimer Disease Assessment Scale (ADAS, including word recall, delayed word recall, orientation, word recognition and recall instructions) were chosen as the measurements of memory performance. Spearman’s correlation coefficient ρ was used to compare the hippocampal shape variation with the memory scores, which were corrected for age in the experiments. For comparison, we also project the shape samples onto the direction of mean difference ¯ XNC − ¯ XAD, and perform the correlation analysis between the memory scores and the shape component along the mean difference direction. 5.5 Experimental Results 5.5.1 Materials The hippocampal volumes used in the experiments are segmented semi-automatically by SNT provided by ADNI (see §3.4.2.1). The hippocampal volumes were divided into a training set and a testing set. The demographic information is listed in Table 5.1. 5.5.2 SSM Based on the choice of the transformations aligning the surfaces in the localization step and in the shape modeling step, we can derive the variation modes of the
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UNIVERSITE DE BOURGOGNE U.F.R. SCIE
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Résumé L’objectif de cette thè
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TABLE DES MATIÈRES Acknowledgement
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BIBLIOGRAPHY 173 Petersen, R. C., S
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BIBLIOGRAPHY 177 Shen, K., Bourgeat
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BIBLIOGRAPHY 179 Barillot, C., Hayn
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BIBLIOGRAPHY 181 Tzourio-Mazoyer, N
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BIBLIOGRAPHY 183 Vos, F. M., de Bru
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BIBLIOGRAPHY 185 Woolrich, M. W., J
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