Semi-supervised learning of anatomical manifolds for atlas-based segmentation of medical images

This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi-supervised learning of a graph describing a manifold of anatomical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p <; 0.001) was obtained compared to direct atlas-based registration.