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

130 Chapter 5 Quantitative shape analysis of hippocampus in AD
Spearman's ρ
0.20 0.30 0.40 0.50
Logical Memory, immediate recall
M R, Left MR, Right MS, Left MS, Right
(a) Logical memory, immediate recall
Spearman's ρ
0.20 0.30 0.40 0.50
ADAS Memory Subscores
M R, Left MR, Right MS, Left MS, Right
(c) Memory subscores of ADAS
Spearman's ρ
Spearman's ρ
0.20 0.30 0.40 0.50
Logical Memory, delayed recall
M R, Left MR, Right MS, Left MS, Right
(b) Logical memory, delayed recall
0.20 0.25 0.30 0.35 0.40 0.45
AVLT
M R, Left MR, Right MS, Left MS, Right
(d) AVLT
. .All landmarks; . .LR, p < 0.1; . .LR, p < 0.01;
. .LS, p < 0.1; . .LS, p < 00.1; . .Mean difference.
Figure 5.11: Bar plots of Spearman’s ρ of the correlation between the memory
indices and the principal components from each model. The correlation analysis
is performed on the modes extracted from all landmarks, landmarks selected
with thresholds at α = 0.1, and landmarks thresholded at α = 0.01. The
component with strongest correlation with the memory score is plotted. The
correlation of the shape component along the mean difference direction is also
plotted.
Since the sizes of samples in LS were normalized, the global difference in the size
between subpopulations was filtered out. The resulting subregions found by LS to
be significantly different between AD and controls were more localized to subfields
such as CA1 and subiculum.
The best classification performance was obtained by the models describing local
size-and-shape variations (LS + MR). It is noticed that the shape changes may
appear on a local scale as deformation involving both size and shape of the sub-
region. The localized size-and-shape model MR on hippocampal subregions can
therefore detect the shape changes in the form of local volume change which were