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

Chapter 2 Literature Review 27
In the registration step, commonly used NRR framework based on maximization of
context-free image similarity measure does not ensure correct correspondences be-
tween the image-pair (Crum et al., 2003). The mismatch of features is propagated
into the result, leading to segmentation errors. In the comparison by Carmichael
et al. (2005), NRR methods (Friston et al., 1995; Woods et al., 1998; Chen, 1999)
fit better to the complex shape of hippocampus than using only affine transfor-
mations (Friston et al., 1995; Woods et al., 1998; Jenkinson et al., 2002). The use
of multiple atlases with ANIMAL techniques has been described by Collins and
Pruessner (2010). Yassa and Stark (2009) evaluated current registration methods,
including Talairach alignment and 3dWarpDrive in AFNI (Cox, 1996), normal-
ization in SPM (Ashburner and Friston, 1999, 2005), LDDMM (Beg et al., 2005;
Miller et al., 2005), Diffeomorphic Anatomical Registration Through Exponen-
tial Lie Algebra (DARTEL, Ashburner, 2007), and Demons (Thirion, 1998; Ver-
cauteren et al., 2009), in terms of their performance in aligning the structures in
the medial temporal lobe. The Demons algorithm was found to perform the best
in aligning the hippocampus.
The issue of multi-atlas selection has been discussed by Aljabar et al. (2007, 2009),
in which the similarity based atlas selection strategies are classified into three cat-
egories, namely segmentation similarity, image similarity, and demographics, with
the selection by image similarity ranking being the most popular in applications.
The overlap between the segmentation result and the ground truth in multi-atlas
segmentation, measured by DSC, reaches its peak when approximately 10 to 20
similarity ranked atlases are selected, and combined by a vote rule (Aljabar et al.,
2007), as opposed to the convergence of the DSC, as the number of the atlases
randomly selected increases. When less similar atlases are fused to the segmen-
tation result, the information relevant to the segmentation is outweighed by the
misalignment propagated to the result.
With regard to the label fusion, the basic method is to combine the transformed
label maps by voxel-wise majority voting, and the final labeling of the voxel is