Atlas-guided segmentation of brain images via optimizing neural networks

Automated segmentation of magnetic resonance (MR) brain imagery into anatomical regions is a complex task that appears to need contextual guidance in order to overcome problems associated with noise, missing data, and the overlap of features associated with different anatomical regions. In this work, the contextual information is provided in the form of an anatomical brain atlas. The atlas provides defaults that supplement the low-level MR image data and guide its segmentation. The matching of atlas to image data is represented by a set of deformable contours that seek compromise fits between expected model information and image data. The dynamics that deform the contours solves both a correspondence problem (which element of the deformable contour corresponds to which elements of the atlas and image data?) and a fitting problem (what is the optimal contour that corresponds to a compromise of atlas and image data while maintaining smoothness?). Some initial results on simple 2D contours are shown.