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

Abstract
The aim of this thesis is to investigate the shape change in hippocampus due
to the atrophy in Alzheimer’s disease (AD). To this end, specific algorithms and
methodologies were developed to segment the hippocampus from structural magnetic
resonance (MR) images and model variations in its shape.
We use a multi-atlas based segmentation propagation approach for the segmentation
of hippocampus which has been shown to obtain accurate parcellation of brain
structures. We developed a supervised method to build a population specific atlas
database, by propagating the parcellations from a smaller generic atlas database.
Well segmented images are inspected and added to the set of atlases, such that the
segmentation capability of the atlas set may be enhanced. The population specific
atlases are evaluated in terms of the agreement among the propagated labels when
segmenting new cases. Compared with using generic atlases, the population specific
atlases obtain a higher agreement when dealing with images from the target
population.
Atlas selection is used to improve segmentation accuracy. In addition to the conventional
selection by image similarity ranking, atlas selection based on maximum
marginal relevance (MMR) re-ranking and least angle regression (LAR) sequence
are developed for atlas selection. By taking the redundancy among atlases into
consideration, diversity criteria are shown to be more efficient in atlas selection
which is applicable in the situation where the number of atlases to be fused is
limited by the computational resources.
Given the segmented hippocampal volumes, statistical shape models (SSMs) of
hippocampi are built on the samples to model the shape variation among the
population. The correspondence across the training samples of hippocampi is
established by a groupwise optimization of the parameterized shape surfaces. The
spherical parameterization of the hippocampal surfaces are flatten to facilitate
the reparameterization and interpolation. The reparameterization is regularized
by viscous fluid, which is solved by a fast implementation based on discrete sine
transform.
In order to use the hippocampal SSM to describe the shape of an unseen hippocampal
surface, we developed a shape parameter estimator based on the expectationmaximization
iterative closest points (EM-ICP) algorithm. A symmetric data
term is included to achieve the inverse consistency of the transformation between
the model and the shape, which gives more accurate reconstruction of the shape
from the model. The shape prior modeled by the SSM is used in the maximum
a posteriori estimation of the shape parameters, which is shown to enforce the
smoothness and avoid the effect of over-fitting.
In the study of the hippocampus in AD, we use the SSM to model the hippocampal
shape change between the healthy control subjects and patients diagnosed
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