Supervised learning for brain mr segmentation via fusion of partially labeled multiple atlases

Yan Deng, Yan Deng

Abstract: Fully labeled manual segmentation — a cornerstone of neuro-anatomical structure segmentation, is known to be a tedious, time-consuming and error-prone task even for trained experts. In this paper, we propose a novel partially labeled multiple atlas-based segmentation algorithm which can simultaneously segment multiple structures from a given image. Intra- and Inter-structural constraints are imposed to preserve spatial relationships and to propagate the segmentation from the labeled regions to the unlabeled regions. We present several experiments on real data sets which show that our approach yields accurate segmentations of the test data even in the absence of a large percentage of the atlas labels. Further, our approach has the ability to refine the given partially labeled atlases via a supervised learning stage.