Abstract
Accurate automated brain structure segmentation
methods facilitate the analysis of large-scale neuroimaging studies.
This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information
about a structure’s location and appearance. The spatial model
is implemented by registering multiple atlas images to the target
image and creating a spatial probability map. The structure’s appearance is modeled by a classi¿er based on Gaussian scale-space
features. These components are combined with a regularization
term in a Bayesian framework that is globally optimized using
graph cuts. The incorporation of the appearance model enables the
method to segment structures with complex intensity distributions
and increases its robustness against errors in the spatial model.
The method is tested in cross-validation experiments on two
datasets acquired with different magnetic resonance sequences,
in which the hippocampus and cerebellum were segmented by
an expert. Furthermore, the method is compared to two other
segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces
accurate results with mean Dice similarity indices of 0.95 for the
cerebellum, and 0.87 for the hippocampus. This was comparable to
or better than the other methods, whereas the proposed technique
is more widely applicable and robust.
methods facilitate the analysis of large-scale neuroimaging studies.
This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information
about a structure’s location and appearance. The spatial model
is implemented by registering multiple atlas images to the target
image and creating a spatial probability map. The structure’s appearance is modeled by a classi¿er based on Gaussian scale-space
features. These components are combined with a regularization
term in a Bayesian framework that is globally optimized using
graph cuts. The incorporation of the appearance model enables the
method to segment structures with complex intensity distributions
and increases its robustness against errors in the spatial model.
The method is tested in cross-validation experiments on two
datasets acquired with different magnetic resonance sequences,
in which the hippocampus and cerebellum were segmented by
an expert. Furthermore, the method is compared to two other
segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces
accurate results with mean Dice similarity indices of 0.95 for the
cerebellum, and 0.87 for the hippocampus. This was comparable to
or better than the other methods, whereas the proposed technique
is more widely applicable and robust.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | IEEE Transactions on Medical Imaging |
| Vol/bind | 31 |
| Udgave nummer | 2 |
| Sider (fra-til) | 276-286 |
| Antal sider | 11 |
| ISSN | 1558-254X |
| DOI | |
| Status | Udgivet - feb. 2012 |