Docteur de l'université Automatic Segmentation and Shape Analysis ...
Docteur de l'université Automatic Segmentation and Shape Analysis ...
Docteur de l'université Automatic Segmentation and Shape Analysis ...
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
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.