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

70 Chapter 3 Hippocampal segmentation using multiple atlases
40 population specific atlases were evaluated in terms of their agreement when
propagated and fused to target images. Comparing to the 18 images in the IBSR
atlases, the population specific atlas set built from the elderly population reaches
a higher level of consensus generic IBSR atlases. The result segmentations are
produced by a majority vote rule with higher certainty. Although as a supervised
approach our method requires visual inspection, it is still less time consuming and
costs less than manually segment images.
LWV label fusion gives better accuracy by utilizing the local image similarity infor-
mation, which makes it possible to improve the segmentation quality by increasing
the number of atlases. It is necessary to select the registration results when the
number of atlas to be fused is limited. As a combination optimization problem,
selection by exhaustive search is not tractable. Conventional heuristics such as
similarity ranking selects the atlases most close to the query image while does
not consider the inter-atlas redundancy. Selecting atlases re-ranked according to
MMR and LAR re-ranking is more efficient compared to image similarity selection
when labels are fused by LWV. They provide more accurate results when the same
number of atlases are selected and fused. These methods are advantageous when
the number the atlases to be fused is limited by the computation time, memory
constraint and/or the size of atlas set. In the future work, it will be of interest
to search for the selection of atlases based on the combination of current methods
in order to optimize the performance of the label fusion over varying sizes of the
atlas set.