  This paper proposes an automatic method for scapula bone segmentation from
Magnetic Resonance (MR) images using deep learning. The purpose of this work is
to incorporate anatomical priors into a conditional adversarial framework,
given a limited amount of heterogeneous annotated images. Our approach
encourages the segmentation model to follow the global anatomical properties of
the underlying anatomy through a learnt non-linear shape representation while
the adversarial contribution refines the model by promoting realistic
delineations. These contributions are evaluated on a dataset of 15 pediatric
shoulder examinations, and compared to state-of-the-art architectures including
UNet and recent derivatives. The significant improvements achieved bring new
perspectives for the pre-operative management of musculo-skeletal diseases.
