Abstract
Purpose: By incorporating high-level shape priors, atlas-based segmentation has achieved tremendous success
in the area of medical image analysis. However, the effect of various kinds of atlases, e.g., average shape model,
example-based multi-atlas, has not been fully explored. In this study, we aim to generate different atlases and
compare their performance in segmentation.
Methods: We compare segmentation performance using parametric deformable model with four different atlases,
including 1) a single atlas, i.e., average shape model (SAS); 2) example-based multi-atlas (EMA); 3) cluster-based
average shape models (CAS); 4) cluster-based statistical shape models (average shape + principal shape variation
modes)(CSS). CAS and CSS are novel atlases constructed by shape clustering. For comparison purpose, we also
use PDM without atlas (NOA) as a benchmark method.
Experiments: The experiment is carried on liver segmentation from whole-body CT images. Atlases are
constructed by 39 manually delineated liver surfaces. 11 CT scans with ground truth are used as testing data
set. Segmentation accuracy using different atlases are compared.
Conclusion: Compared with segmentation without atlas, all of the four atlas-based image segmentation methods
achieve better results. Multi-atlas based segmentation behaves better than single-atlas based segmentation. CAS
exhibit superior performance to all other methods.
