Groupwise combined segmentation and registration for atlas construction
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Fusing adaptive atlas and informative features for robust 3D brain image segmentation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
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Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject. Our model combines anatomical priors with the subject's own anatomy, resulting in a subject specific atlas which we call an "adaptive atlas". The generation of this adaptive atlas does not require the subject to have an anatomy similar to that of the atlas population, nor does it rely on the availability of an ensemble of similar images. The proposed method shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.