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

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26 Chapter 2 Literature Review<br />

methods (Murgasova et al., 2006, 2007), <strong>and</strong> FreeSurfer whole brain segmentation<br />

(Fischl et al., 2002).<br />

The use of multiple atlases manually traced by experts improves the accuracy of the<br />

segmentation based on a single atlas (Rohlfing et al., 2004a,b; Heckemann et al.,<br />

2006), by reducing the bias towards each single atlas, <strong>and</strong> taking the advantage of<br />

a priori knowledge enco<strong>de</strong>d in the atlas set. Multiple atlases are transformed from<br />

the coordinates in the space of atlas to the target image to be segmented by pair-<br />

wise non-rigid registration (NRR). The labeling of each voxel in the target image<br />

is then <strong>de</strong>termined by fusing the warped segmentations of the c<strong>and</strong>idate atlases,<br />

usually by a vote rule. The errors ma<strong>de</strong> by individual atlas may be corrected by<br />

other atlases in the label fusion. It has been shown to be a viable approach to the<br />

<strong>de</strong>lineation of subcortical structures in brain MR images (Babalola et al., 2008).<br />

The inter-subject variability may exceed the transformation space searched by the<br />

NRR algorithms, such that the error of mismatch between the image <strong>and</strong> the atlas<br />

may be propagated to the segmentation result. Registration failure will lead to the<br />

mislabeling. The errors in the result of multi-atlas segmentation thus arise either<br />

from the atlases used, or due to the misregistration. In practice, the accuracy of<br />

the method <strong>de</strong>pends on the following <strong>de</strong>sign choices<br />

• the initial atlas set<br />

• the registration algorithms<br />

• the atlas selection strategy after the registration<br />

• <strong>and</strong> the label fusion method.<br />

A representative but unbiased set of atlases is usually chosen to target the popu-<br />

lation of interest. Cohort atlases from the population are found to perform better<br />

than using a single st<strong>and</strong>ard atlas (Carmichael et al., 2005). Though it is not<br />

always readily available, a larger atlas set to cope with the variability in a diverse<br />

population (Wolz et al., 2010) or a specific sub-population (Shen et al., 2010). It<br />

can be obtained by propagation from a smaller set of manually segmented atlases.

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