Landmark detection and multiorgan segmentation: Representations and supervised approaches

In this chapter we present discriminative learning approaches for landmark detection and shape segmentation. Specifically, we elaborate different landmark representations and demonstrate how to use them in different supervised learning methods. We then present various shape representations and a learning approach that fuses regression, which models global context, and classification, which models local context, for rapid multiple organ segmentation.