Segmentation of brain images using adaptive atlases with application to ventriculomegaly

Abstract

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.

Fig. 1

Results of an atlas-based GMM segmentation algorithm on a subject with large ventricles (sulcal-CSF, ventricles, GM, and WM are represented by dark red, light red, orange, and white, respectively).

Fig. 2

Evolution of the adaptive atlas from the initial statistical atlas. It originally is biased by the training data but eventually converges to the subject’s geometry.

Fig. 3

Comparison of the adaptive atlas approach and three other atlas-based segmentation algorithms on hydrocephalus subjects with moderate (top row) and marked (bottom row) ventriculomegaly(sulcal-CSF, ventricles, GM, and WM are represented by dark red, light red, orange, and white, respectively. Yellow represents WM-hypointenisty in Freesurfer segmentation).